Discussion: Treatment of Substance Use Disorders Of the substance disorders, alcohol-related disorde

Vulnerability for Alcohol Use Disorder and Rate of
Alcohol Consumption
Joshua L. Gowin, Ph.D., Matthew E. Sloan, M.D., M.Sc., Bethany L. Stangl, Ph.D., Vatsalya Vatsalya, M.D., M.Sc.,
Vijay A. Ramchandani, Ph.D.

Objective: Although several risk factors have been identified
for alcohol use disorder, many individuals with these factors
do not go on to develop the disorder. Identifying early
phenotypic differences between vulnerable individuals and
healthy control subjects could help identify those at higher
risk. Binge drinking, defined as reaching a blood alcohol level
of 80 mg%, carries a risk of negative legal and health out-
comes and may be an early marker of vulnerability. Using a
carefully controlled experimental paradigm, the authors
testedthehypothesisthatriskfactorsforalcoholusedisorder,
including family history of alcoholism, male sex, impulsivity,
and low level of response to alcohol, would predict rate of
binging during an individual alcohol consumption session.

Method: This cross-sectional study included 159 young so-
cial drinkers who completed a laboratory session in which
they self-administered alcohol intravenously. Cox proportional

hazards models were used to determine whether risk factors
for alcohol use disorder were associated with the rate of
achieving a binge-level exposure.

Results:Agreaterpercentageofrelativeswithalcoholism(hazard
ratio: 1.04, 95% CI=1.02–1.07), male sex (hazard ratio: 1.74, 95%
CI=1.03–2.93), and higher impulsivity (hazard ratio: 1.17, 95%
CI=1.00 to 1.37) were associated with a higher rate of binging
throughout the session. Participants with all three risk factors had
the highest rate of binging throughout the session compared
withthelowestriskgroup(hazardratio:5.27,95%CI=1.81–15.30).

Conclusions: Binge drinking may be an early indicator of
vulnerability to alcohol use disorder and should be carefully
assessed as part of a thorough clinical evaluation.

AmJPsychiatry2017;174:1094–1101;doi:10.1176/appi.ajp.2017.16101180

Alcohol use disorder has a lifetime prevalence of nearly one in
threeindividualsintheUnitedStates(1).Animportantgoalisto
identify at-risk individuals prior to the development of this
disorder so that they canbetargetedfor earlyintervention.One
way to determine early phenotypic differences in those at risk
is to examine behavior at the level of an individual drinking
session. For example, the rate of drinking and total alcohol
exposure may differ between those at high and low risk. These
parameters, however, are difficult to quantify in the field be-
cause of the lack of instruments that can continuously and
accurately monitor blood alcohol concentration. Furthermore,
asking individuals to report details about their rate of con-
sumption does not account for variability in absorption and
metabolism (2) and would likely be inaccurate because in-
toxication impairs recall (3). Despite these measurement
difficulties, there is evidence that the rapid consumption of
large quantities of alcohol leading to a blood alcohol con-
centration of 80 mg%, defined as binge drinking (4), affects
psychological and physical well-being. Binge drinking is
associatedwithgreater riskof negativehealth consequences

(e.g., myocardial infarction) and legal trouble (5, 6). Binge
drinking may signify an innate preference for higher brain
alcohol exposure and may begin before an individual meets
criteria for an alcohol use disorder, but this hypothesis has
never been empirically tested.

One method to assess alcohol consumption that overcomes
many of these measurement difficulties is intravenous alcohol
self-administration (7). This method has shown good test-
retest reliability and external validity (8, 9) and has been
employed in pharmacological (10) and genetic studies (11).
Intravenous alcohol self-administration has several advan-
tages over oral self-administration. Whereas oral adminis-
tration at fixed doses can result in up to threefold variability
in alcohol exposure between individuals as a result of phar-
macokinetic differences (12, 13), intravenous administration
standardizes alcohol exposure by bypassing gastrointestinal
absorption and first-pass metabolism. Interindividual differ-
ences in alcohol distribution and elimination are accounted
for by using an infusion algorithm that adjusts for age, sex,
height, and weight (2). Accordingly, each infusion increases

See related features: Editorial by Dr. Petrakis (p. 1034), Clinical Guidance (Table of Contents), CME course (p. 1127), AJP Audio (online),
and Video by Dr. Pine (online)

1094 ajp.psychiatryonline.org Am J Psychiatry 174:11, November 2017

ARTICLES

alcohol levels by a fixed quantity, allowing the
infusion software to provide continuous esti-
mates of blood alcohol levels that closely track
brain alcohol exposure (14) and breathalyzer
readouts(15).Theseestimatescanthenbeusedto
measureanindividual’stotalalcoholexposure,as
wellashowquicklytheindividualreachesabinge
level of exposure. This paradigm also eliminates
specific cues associated with oral alcoholic bev-
erages, including taste, smell, andappearance.As
a result, intravenous self-administration should
be driven primarily by alcohol’s pharmacody-
namic effects, such as dopamine release in the
nucleus accumbens (16). This method is there-
fore ideal to determine whether preference for
higher alcohol exposure is evident prior to the
development of alcohol use disorder among in-
dividuals with biological risk factors.

The DSM-5 lists the following genetic and
physiological risk factors for alcohol use dis-
order (17): family history of alcoholism (18),
male sex (1), impulsivity (19), absence of acute
alcohol-related skin flush (20), pre-existing
schizophrenia or bipolar disorder (21), and low
levelofresponsetoalcohol(22).Althoughthesefactorsmarkedly
increase the risk of developing alcohol use disorder, it remains
unclear how they affect the likelihood of risky drinking patterns
prior to disorder onset. In the present study, we examined the
largest community sample to date of young adult social drinkers
using intravenous alcohol self-administration. We investigated
whetherthegeneticandphysiologicalriskfactorslistedinDSM-5
(except for skin flush and comorbid psychiatric disorders, which
were exclusion criteria) were associated with the rate of binge-
level exposure during an individual drinking session. We hy-
pothesized that individuals at higher risk for developing an al-
cohol use disorder would exhibit a preference for higher brain
alcohol exposure as demonstrated by higher rates of binging
throughoutthesessionandhigherlevelsoftotalalcoholexposure.

METHOD

Participant Characteristics
A total of 162 social drinkers between the ages of 21 and
45 were recruited through newspaper advertisements and
the National Institutes of Health (NIH) Normal Volunteer
Office (for detailed demographic information, see Table 1 and
Tables S1–S3 in the data supplement accompanying the online
version of this article). To be included, participants must have
consumed at least five drinks on one occasion at one point in
their life. Participants completed a telephone screen and sub-
sequently completedan in-personassessmentattheNIHClinical
Center in Bethesda, Md. The study protocol was approved by
the NIH Addictions Institutional Review Board, and participants
were enrolled after providing written, informed consent.

Participants were excluded if they met any of the fol-
lowing 10 exclusion criteria: 1) nondrinker; 2) lifetime history

of mood, anxiety, or psychotic disorder; 3) current or lifetime
history of substance dependence (including alcohol and
nicotine); 4) recent illicit use of psychoactive substances;
5) history of acute alcohol-related skin flush; 6) regular to-
bacco use (.20 uses/week); 7) history of clinically significant
alcohol withdrawal; 8) lifetime history of suicide attempts; 9)
current or chronic medical conditions, including cardiovascular
conditions, requiring inpatient treatment or frequent medical
visits; or 10) use of medications that may interact with alcohol
within 2 weeks prior to the study. Females were excluded if they
were breastfeeding or pregnant or if they intended to become
pregnant.

All participants were assessed for psychiatric diagnoses,
history of acute alcohol-related skin flush, drinking history,
andotherriskfactorsforalcoholusedisorder.Diagnoseswere
assessed by the Structured Clinical Interview for DSM-IV Axis I
disorders (23). History of acute alcohol-related skin flush was
assessedusingtheAlcoholFlushingQuestionnaire(24).Drinking
history was assessed using the Alcohol Use Disorder Identifi-
cation Test (25). Two participants were excluded from this
analysisbecausetheywereheavydrinkersbasedontheTimeline
Followback Interview (.20 drinks/week for males, .15 drinks/
week for females). One participant was excluded because
software failure caused the session to be terminated prior
to minute 20 of the alcohol self-administration session,
resulting in a final sample size of 159 participants.

Alcohol Use Disorder Risk Factor Measures
Family history. Participants completed the Family Tree
Questionnaire (26) to identify first- and second-degree rel-
atives who may have had alcohol-related problems. They
subsequently completed the family history assessment plus

TABLE 1. Characteristics of the Sample by Sex

Characteristic Male (N=86) Female (N=73)

Mean SD Mean SD

Age (years) 26.4 5.2 25.8 5.0
Family history densitya,b 3.6 8.5 2.6 6.9
Delay discountinga,c –4.7 1.8 –4.5 1.7
Level of alcohol responsed,e 4.8 2.1 3.7 1.7
Alcohol Use Disorder Identification Test score 5.8 2.5 5.1 2.8

N % N %

Family history positive 17 19.8 11 15.1
Current alcohol abusea 2 2.4 2 2.7

a Data were missing for some participants (family history, N=158; delay discounting, N=134; current
alcohol abuse, N=158).

b Family history density was obtained by dividing the number of first- and second-degree relatives
withanalcoholusedisorderbythetotalnumberoffirst-andsecond-degreerelatives;it isreported
as a percentage. The value displayed represents the mean and SD for the whole sample (see Table
S1 in the online data supplement for family history density in the family history positive group).

c Delaydiscountingisabehavioralmeasureofimpulsivityinwhichparticipantschoosebetweensmaller
immediate or larger delayed rewards; values are reported as the natural logarithm of the discounting
constant, k; lower values of ln(k) indicate lower degrees of delay discounting and less impulsivity.

d Level of alcohol response is derived from the Self-Rating of the Effects of Alcohol form, assessing
response during the first five drinking occasions; the final score represents the mean of the
number of drinks needed to achieve four possible intoxication-related outcomes, with a higher
number indicating a lower level of response to alcohol.

e Male and female participants showed statistically different distributions for level of alcohol re-
sponse using the Mann-Whitney test (Zu=3.7, p,0.01).

Am J Psychiatry 174:11, November 2017 ajp.psychiatryonline.org 1095

GOWIN ET AL.

individual assessment modules of the Semi-Structured As-
sessment for Genetics of Alcoholism for all identified relatives
(27). This assessment is widely used in family history-based
studies, including large genetic studies, such as the Collabo-
ration on the Genetics of Alcoholism (28). If no information
was availableaboutarelative,thenthatrelativewasscoredasa
0. Relatives with a known history of alcohol-related problems
werescoredasa1.Afamilyhistorydensityscorewascalculated
by dividing the number of relatives with alcohol problems by
the total number of first- and second-degree relatives. One
participant did not complete this measure, and his value was
imputed with the sample median of 0 given that family history
density was not normally distributed (Shapiro-Wilk test:
p,0.001). We conducted all models with and without this
participant and found that his exclusion did not alter our
findings, and thus we report the results with this participant
included.

Behavioral impulsivity. Participants completed a delay dis-
counting task (29), which is a well-validated measure of
behavioral impulsivity that has a robust association with
alcohol use disorder (30, 31). During this task, participants
chose between smaller immediate rewards or $100 re-
ceived after a delay (e.g., $90 now or $100 in 7 days). Im-
mediate rewards ranged in value from $0 to $100, and delay
periods ranged from 7 to 30 days. The degree of discount-
ing delayed rewards, k, can be calculated using the equation
developed by Mazur et al. (32). Since k values were not
normally distributed, they were normalized using a loga-
rithmic transformation and reported as ln(k). Lower values
of ln(k) suggest less impulsivity and lower degrees of dis-
counting. A portion of the sample did not complete this task
(N=25), and missing values of ln(k) were imputed with the
sample mean.

Level of response to alcohol. Participants also completed the
Self-Rating of the Effects of Alcohol form (33). This in-
strument assesses response to alcohol during the first five
drinking occasions of a person’s life, their heaviest drinking
period, and their most recent drinking period. For each
period, it asks how many drinks it took for them to feel
different, to feel dizzy, to begin stumbling, and to pass out.
The final score represents the mean of the number of drinks
needed to achieve each outcome, with a higher number of
drinks indicating a lower level of response to alcohol. We
focused on the first five drinking occasions in the present
analyses to reduce the potentially confounding impact of
tolerance.

Intravenous Alcohol Self-Administration
Participants were instructed not to drink alcohol in the
48 hours prior to study procedures. Upon arrival, they
provided a breathalyzer reading to confirm abstinence.
Participants also provided a urine sample that was tested for
illicit drugsand,for females, pregnancy; both hadto benegative
to proceed with the study session. After the participant ate a

standardized (350 kcal) meal, an intravenous catheter was
inserted into a vein in the forearm. Self-administration was
conducted using the computer-assisted alcohol infusion
systemsoftware,whichcontrolledtherateofinfusionof6.0%
v/v alcohol in saline for each individual using a physiologi-
cally based pharmacokinetic model for alcohol distribu-
tion and metabolism that accounts for sex, age, height, and
weight (2).

The alcohol self-administration session consisted of a
25-minuteprimingphaseanda125-minutefree-accessphase.
During the first 10 minutes of the priming phase, participants
were required to push a button four times at 2.5-minute
intervals. Each button press resulted in an alcohol infu-
sion that raised blood alcohol concentration by 7.5 mg% in
2.5 minutes, such that participants achieved a peak con-
centration of approximately 30 mg% at minute 10. During the
next 15 minutes, the button remained inactive while par-
ticipants experienced the effects of the alcohol. At minute 25,
thefree-accessphasebegan,andparticipantswereinstructed
to “try to recreate a typical drinking session out with friends.”
Participants could self-administer ad libitum, but they had
to wait until one infusion was completed before initiating
another. Blood alcohol concentration was estimated con-
tinuously by the software based on infusion rate and model-
estimated metabolism, and a readout was provided at
30-second intervals. Breath alcohol concentration was also
obtained via breathalyzer at 15-minute intervals to confirm
the software-calculated estimates; these readings were en-
tered into the software to provide the model feedback, and
the infusion rate was automatically adjusted accordingly (2).
Software estimates of blood alcohol concentration were
used to determine whether a participant reached binge-
level exposure, defined as achieving an estimated blood
alcohol concentration greater than 80 mg% (4). A limit was
imposed such that estimated blood alcohol concentration
could not exceed 100 mg% to prevent adverse events due
to intoxication.

Statistical Analysis
To examine whether risk factors for alcohol use disorder
were predictors of rate of binging throughout the free-
access phase of the intravenous alcohol self-administration
session, we plotted Kaplan-Meier survival curves and
conducted Cox proportional hazards models.We generated
the following four Kaplan-Meier survival curves using
binary variables (Figure 1): 1) male compared with female;
2) family-history positive compared with negative; 3) high
compared with low impulsivity (median split); and 4) high
compared with low level of response to alcohol (median
split). For the Cox proportional hazards analyses, the
outcome variable was time to binge (estimated blood al-
cohol concentration of 80 mg%), and participants were
censored when they reached a binge or ended the session
early (one participant). For the initial Cox proportional
hazards model, five independent variables were included:
sex was coded as a binary variable (0 for females, 1 for

1096 ajp.psychiatryonline.org Am J Psychiatry 174:11, November 2017

VULNERABILITY FOR ALCOHOL USE DISORDER AND RATE OF CONSUMPTION

males), and delay discounting, family history density, level
of response to alcohol, and age were entered as continuous
variables.

To determine whether faster rate of consumption
translated into greater overall exposure to alcohol, we cal-
culated the area under the curve for the estimated breath
alcohol concentration by time plot during the free-access
phase of the session. Three individuals ended the session
early due to software malfunction or adverse events (at
minutes 59, 88.5, and 99.5); thus, in order to generate the area
under the curve for these participants, we imputed values for

the remainder of the session by carrying their last observed
alcohol concentration forward. To confirm the validity of
this approach, we applied the same imputation procedure
for 20 random participants starting at minute 59 and found
that the imputed values correlated highly with the actual
values(Spearman’srho.0.9).WeconductedMann-Whitney
tests to compare area under the curve distributions for each
risk factor, as area under the curve values were not normally
distributed (Shapiro-Wilk test: p,0.05). For these analy-
ses, we used the binary categorical risk factors described
above.

FIGURE 1. Cumulative Probability of Achieving Binge-Level Exposure by Each Alcohol Use Disorder Risk Factora

Time (minutes)

1200 20 40 60 80 100

1200 20 40 60 80 100

1200 20 40 60 80 100

1200 20 40 60 80 100

P
e

rc
e

n
t

R
e

a
c

h
in

g
B

in
g

e

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

Low (N=67)

High (N=67)

Censored

Time (minutes)
P

e
rc

e
n

t
R

e
a

c
h

in
g

B
in

g
e

Negative (N=130)

Positive (N=28)

Censored

Time (minutes)

P
e

rc
e

n
t

R
e

a
c

h
in

g
B

in
g

e

High (N=86)

Low (N=73)

Censored

Time (minutes)

P
e

rc
e

n
t

R
e

a
c

h
in

g
B

in
g

e

F (N=73)

M (N=86)

Censored

Delay Discounting Family History

Level of Response to Alcohol Sex

a Cumulative probability of achieving a binge-level exposure (estimated breath alcohol concentration of 80 mg%) was higher in males compared
with females, in family-history positive compared with family-history negative individuals, in high compared with low delay discounters, and in low
compared with high responders to alcohol.

Am J Psychiatry 174:11, November 2017 ajp.psychiatryonline.org 1097

GOWIN ET AL.

To assess the additive effects of significant variables
from the aforementioned analyses, we coded individuals
according to their number of risk factors for alcohol use
disorder. For this analysis, we only used the binary risk
factors described above, excluding level of response to al-
cohol, which did not contribute to the aforementioned
models. We thus created four groups: zero-, one-, two-, and
three-risk factor groups. The zero-risk factor group served
as the reference group. We plotted Kaplan-Meier survival
curves to examine differences between groups and also to fit
a Cox proportional hazards model additionally adjusted for
age. We also tested whether there was evidence of additive
effects of risk factors on overall alcohol exposure during the
session by comparing the area under the curve values for
different risk groups using a Jonckheere-Terpstra test
(34, 35).

RESULTS

Effect of Risk Factors on Rate of Binging
Overall, 60 participants achieved a binge-level exposure, and
99 participants had estimated blood alcohol concentrations
beneath 80 mg% across the entire session. A higher per-
centage of bingers was found in family-history positive
compared with negative individuals (57.1% and 33.1%,
respectively), males compared with females (43.0% and 31.5%,
respectively), high compared with low delay-discounting in-
dividuals (49.3% and 29.9%, respectively), and those with a
low compared with high level of response to alcohol (43.8%
and 32.6%, respectively) (Figure 1).

We tested whether risk factors for alcohol use disorder
predicted the rate of binging throughout the session using
a Cox proportional hazards model with all four risk factors
and age as independent variables (model 1). Family history
density was a significant predictor (hazard ratio=1.04, 95%
confidence interval [CI]=1.02–1.07, p=0.001), whereas male
sex (hazard ratio=1.71, 95% CI=1.00–2.94, p=0.052) and delay
discounting (hazard ratio=1.17, 95% CI=1.00–1.37, p=0.056)
were marginally significant. Level of response to alcohol was
not a significant predictor of the rate of binging throughout
the session (hazard ratio=1.01, 95% CI=0.89–1.15, p=0.840)
(Table 2). Because the level of response was not contribut-
ing to the model and was significantly correlated with sex
(Spearman’s rho=0.29, see Table S4 in the online data
supplement), we dropped it from the model. In this second
analysis (model 2), male sex (hazard ratio=1.74, 95%
CI=1.03–2.93, p=0.038), delay discounting (hazard ratio=1.17,
95% CI=1.00–1.37, p=0.048), and family history density
(hazard ratio=1.04, 95% CI=1.02–1.07, p=0.002) all signifi-
cantly predicted binge rate throughout the session. The
effects of these risk factors remained consistent when
controlling for the Alcohol Use Disorder Identification Test
score (model 3). As would be expected, participants with a
higher Alcohol Use Disorder Identification Test score were
more likely to binge (hazard ratio=1.14, 95% CI=1.04–1.24,
p=0.004).

Effects of Individual Risk Factors on Total
Alcohol Exposure
We also tested whether each individual risk factor was as-
sociated with total alcohol exposure as measured by the area
under the estimated blood alcohol concentration versus time
curve during the free-access phase. Median alcohol exposure
was higher in family-history positive individuals, males, and
participants with delay-discounting scores above the median
(see Figure S1 in the online data supplement), with sig-
nificantly different distributions across sex and delay-
discounting groups and marginal significance across family
history groups (family history: U[28, 130]=2247, p=0.052; sex:
U[86, 73]=3763, p=0.031; delay discounting: U[67, 67]=2839,
p=0.008). There was no significant difference between those
with high and low levels of alcohol response (U[73, 86]=2619,
p=0.072).

Additive Effects of Risk Factors on Rate of Binging
To investigate whether the significant risk factors from the
prior analysis had additive effects, we divided participants
basedontheirnumberofriskfactorsintofourgroups:zerorisk
factors (N=26), one risk factor (N=65), two risk factors (N=36),
and three risk factors (N=8), where zero risk factors indicates
a family-history negative female with a delay-discounting score
below the median (Figure 2) (see Table S5 in the online data
supplement for characteristics of the sample by risk factor
group). Cox proportional hazards regression controlling
for age demonstrated that compared with the zero-risk
factors group, individuals in the two-risk factors group
(hazard ratio=2.54,95%CI=1.05–6.12,p=0.038)andthree-risk
factors group (hazard ratio=5.27, CI=1.81–15.30, p=0.002)
binged at higher rates throughout the session. The zero-
risk factors group and the one-risk factor group did not
differ (hazard ratio=1.29, 95% CI=0.55–3.04, p=0.562).
These effects remained significant when controlling for the
level of alcohol response as a continuous variable and the
AlcoholUseDisorderIdentificationTestscore(seeTableS6in
the online data supplement).

Additive Effects of Risk Factors on Total
Alcohol Exposure
Individuals with a greater number of risk factors achieved
higher levels of alcohol exposure, with median area under
the curve values of 2132.5 mg%*min, 3814.8 mg%*min,
4565.7 mg%*min, and 7208.5 mg%*min for individuals
with the lowest to highest number of risk factors, respec-
tively. The results of a Jonckheere-Terpstra test for ordered
alternatives indicated that there was a significant effect
of number of risk factors on the distribution of area under
thecurvevalueswithasmall-to-mediumeffectsize(TJT=3746.0,
p=0.001, Kendall’s t=0.22) (Figure 3). Bonferroni-corrected
pairwise comparisons indicated that the distribution of
the areas under the curve for the two- and three-risk
factors groups were significantly different than that of
the zero-risk factors group, and the three-risk factors
group distribution of area under the curve values also

1098 ajp.psychiatryonline.org Am J Psychiatry 174:11, November 2017

VULNERABILITY FOR ALCOHOL USE DISORDER AND RATE OF CONSUMPTION

differed from that of the one-risk factor group (all p
values ,0.05).

DISCUSSION

Young social drinkers at risk for an alcohol use disorder had
consumption patterns that were markedly different from
low-risk drinkers during a free-access intravenous alcohol
self-administration session. Vulnerable drinkers had higher
rates of binging throughout the session and greater overall
exposure to alcohol. The effects of these risk factors were
additive. This finding is especially remarkable given the
similarity of Alcohol Use Disorder Identification Test scores
between the higher- and lower-risk groups and given that
these effects remained largely unchanged when control-
ling for test scores. To our knowledge, this is the first large
pharmacokinetically controlled study to show that the
presence of risk factors for alcohol use disorder leads to
different patterns of drinking at the level of an individual
drinking session in young social drinkers who have not yet
developed the disorder. These findings suggest an innate
neurobiological preference for higher alcohol exposure
that may contribute to alcohol use disorder risk.

Of the factors we examined, family history of alcoholism
was most strongly associated with the rate of binging dur-
ing the session, with a small-to-medium effect size. This
finding is in accordance with epidemiologic studies showing
that up to one-half of the risk of alcoholism is genetic and
corroborates the results of a small intravenous alcohol self-
administration study demonstrating that family-history
positive individuals achieved higher alcohol exposures
(36). Our study extends these intravenous alcohol self-
administration results by showing that participants with
a greater percentage of biological relatives with alcohol
problems were at greater risk. Our study also found higher
rates of alcohol consumption in males compared with fe-
males,which isconsistent with arecent studyof intravenous
alcohol self-administration in adolescents (9). Delay discount-
ing has previously been observed as a predictor of labo-
ratory alcohol consumption (8), and we confirmed that here.
The level of response to alcohol was not related to the rate
of binging or total alcohol exposure in our study. This may be

partially due to the surprising fact that participants with a
low level of response to alcohol in our study actually had
lower family history densities for alcoholism than partici-
pants with a high level of response (see Table S3 in the online
data supplement), which is the opposite of what has been
found in most studies (37), although controlling for family
history density did not change our results. Level of response
to alcohol may have been influenced by recall bias and may
have shown more predictive power if it had been assessed
experimentally, as in the original studies by Schuckit (22).
Despite some evidence that level of response may vary as a
function of rate of change in blood alcohol concentration
and drinking history (37, 38), we chose to use a simpler static
measure of level of response here. More complex assess-
ments of level of response may yield different results.

There were several limitations to this study, most notably
the cross-sectional design. Longitudinal studies will be

TABLE 2. Hazard Ratios From Cox Proportional Hazards Models Examining the Effect of Alcohol Use Disorder Risk Factors on Rate
of Binginga

Variable

Model 1 Model 2 Model 3

Hazard Ratio 95% CI Hazard Ratio 95% CI Hazard Ratio 95% CI

Family history density (%) 1.04 1.02–1.07 1.04 1.02–1.07 1.04 1.02–1.07
Male sex 1.71 1.00–2.94 1.74 1.03–2.93 1.67 0.99–2.82
Delay discounting 1.17 1.00–1.37 1.17 1.00–1.37 1.17 1.00–1.37
Level of alcohol response 1.01 0.89–1.15 — — — —
Age (years) 0.90 0.83–0.97 0.90 0.83–0.96 0.91 0.85–0.98
Alcohol Use …

The American Psychiatric Association Practice
Guideline for the Pharmacological Treatment of
Patients With Alcohol Use Disorder
Victor I. Reus, M.D., Laura J. Fochtmann, M.D., M.B.I., Oscar Bukstein, M.D., M.P.H., A. Evan Eyler, M.D., M.P.H.,
Donald M. Hilty, M.D., Marcela Horvitz-Lennon, M.D., M.P.H., Jane Mahoney, Ph.D., R.N., PMHCNS-B.C.,
Jagoda Pasic, M.D., Ph.D., Michael Weaver, M.D., Cheryl D. Wills, M.D., Jack McIntyre, M.D. (Consultant),
Jeremy Kidd, M.D. (Consultant), Joel Yager, M.D. (Systematic Review), Seung-Hee Hong (Systematic Review)

At its July 2017 meeting, The APA Board of Trustees ap-
provedtheAPAPracticeGuidelineWritingGroup’s“Practice
Guideline for the Pharmacological Treatment of Patients
with Alcohol Use Disorder.” The full guideline is available at
APA’s Practice Guidelines website.

INTRODUCTION

Thegoalofthisguideline1 istoimprovethequalityofcareand
treatment outcomes for patients with alcohol use disorder
(AUD),asdefinedbyDSM-5(AmericanPsychiatricAssociation,
2013). The guideline focuses specifically on evidence-based
pharmacological treatments for AUD but also includes state-
ments related to assessment and treatment planning that are
an integral part of using pharmacotherapy to treat AUD.
AUD pharmacotherapy is a topic of increasing interest given
the availability of several medications approved by the U.S.
Food and Drug Administration (FDA) for this disorder and
the burden of AUD in the population.

Worldwide, the estimated 12-month adult prevalence of
AUD is 8.5%, with an estimated lifetime prevalence of 20%
(Slade et al., 2016). In the United States (U.S.), AUD has es-
timated values for 12-month and lifetime prevalence of 13.9%
and 29.1%, respectively, with approximately half of individuals
withlifetime AUD having a severedisorder(Grantet al., 2015).
AUD places a significantstrainonboththepersonal andpublic
health of the U.S. population. According to a 2006 Centers for

Disease Control and Prevention-sponsored study (Bouchery
et al., 2011), AUD and its sequelae cost the U.S. $223.5 billion
annually and account for significant excess mortality (Kendler
etal.,2016).Despiteitshighprevalenceandnumerousnegative
consequences, AUD remains undertreated. Effective and
evidence-based interventions are available, and treatment is
associatedwithreductionsintheriskofrelapse(Dawsonetal,
2006) and AUD-associated mortality (Timko et al., 2006).
Nevertheless, fewer than 1 in 10 individuals in the U.S. with a
12-month diagnosis ofAUDreceiveanytreatment(Substance
Abuse and Mental Health Services Administration, 2014;
Grant et al., 2015). Receipt of evidence-based care is even less
common. For example, one study found that of the 11 million
people in the U.S. with AUD, only 674,000 received psycho-
pharmacologicaltreatment(Marketal.,2009).Accordingly,this
practice guideline provides evidence-based statements aimed
at increasing knowledge and the appropriate use of medica-
tions for AUD. The overall goal of this guideline is to enhance
the treatment of AUD for millions of affected individuals, thereby
reducing the significant psychosocial and public health conse-
quences of this important psychiatric condition.

Overview of the Development Process
Since the publication of the Institute of Medicine (IOM; now
known as National Academy of Medicine) report, Clinical
Practice Guidelines We Can Trust (Institute of Medicine,
2011), there has been an increasing focus on using clearly
defined, transparent processes for rating the quality of evi-
dence and the strength of the overall body of evidence in
systematic reviews of the scientific literature. This guideline
was developed using a process intended to be consistent with
the recommendations of the IOM 2011 report, the Principles
for the Development of Specialty Society Clinical Guidelines
(Council of Medical Specialty Societies, 2012), and the require-
ments of the Agency for Healthcare Research and Quality
(AHRQ) for inclusion of a guideline in the National Guidelines
Clearinghouse. Parameters used for the guideline’s systematic
review are included with the full text of the guideline. The
AmericanPsychiatricAssociation(APA)websitefeaturesafull
description of the guideline development process.

1Practice Guidelines are assessments of current scientific and clinical
information provided as an educational service and should not be
considered as a statement of the standard of care or inclusive of all
proper treatments or methods of care and are not continually updated
and may not reflect the most recent evidence. They are not intended
to substitute for the independent professional judgment of the treating
provider.Theultimaterecommendationregardingaparticularassessment,
clinical procedure, or treatment plan must be made by the clinician in light
of the psychiatric evaluation, other clinical data, and the diagnostic
and treatment options available. The guidelines are available on an
“as is” basis, and APA makes no warranty, expressed or implied,
regarding them. APA assumes no responsibility for any injury or
damage to persons or property arising out of or related to any use
of the guidelines.

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Rating the Strength of Research Evidence and
Recommendations
Development of guideline statements entails weighing the
potential benefits and harms of the statement and then
identifying the level of confidence in that determination. This
concept of balancing benefits and harms to determine guideline
recommendations and strength of recommendations is a hall-
mark of GRADE (Grading of Recommendations Assessment,
Development and Evaluation), which is used by multiple pro-
fessional organizations around the world to develop prac-
tice guideline recommendations (Guyatt et al., 2013). With the
GRADE approach, recommendations are rated by assessing
the confidence that the benefits of the statement outweigh
the harms and burdens of the statement, determining the
confidence in estimates of effect as reflected by the quality of
evidence, estimating patient values and preferences (including
whether they are similar across the patient population), and iden-
tifying whether resource expenditures are worth the expected net
benefit of following the recommendation (Andrews et al., 2013).

In weighing the balance of benefits and harms for each
statement in this guideline, our level of confidence is informed
by available evidence, which includes evidence from clinical
trials as well as expert opinion and patient values and prefer-
ences.Evidenceforthebenefitofaparticularinterventionwithin
aspecificclinical contextisidentifiedthroughsystematicreview
and is then balanced against the evidence for harms. In this
regard, harms are broadly defined and might include direct and
indirect costs of the intervention (including opportunity costs)
as well as potential for adverse events from the intervention.

Many topics covered in this guideline have relied on forms
of evidence such as consensus opinions of experienced cli-
nicians or indirect findings from observational studies rather
than research from randomized trials. It is well recognized
that there are guideline topics and clinical circumstances for
which high-quality evidence from clinical trials is not pos-
sible or is unethical to obtain (Council of Medical Specialty
Societies, 2012). The GRADE working group and guidelines
developed by other professional organizations have noted
that a strong recommendation or “good practice statement”
may be appropriate even in the absence of research evidence
when sensible alternatives do not exist (Andrews et al., 2013;
Brito et al, 2013; Djulbegovic et al., 2009; Hazlehurst et al.,
2013). For each guideline statement, we have described the
type and strength of the available evidence that was available
as wellas thefactors,includingpatientpreferences, thatwere
used in determining the balance of benefits and harms.

The authors of the guideline determined each final rating,
as described in the section “Rating the Strength of Research
Evidence and Recommendations,” and each statement is
endorsed by the APA Board of Trustees. A recommendation
(denoted by the numeral 1 after the guideline statement)
indicates confidence that the benefits of the intervention
clearly outweigh harms. A suggestion (denoted by the nu-
meral 2 after the guideline statement) indicates greater un-
certainty. Although the benefits of the statement are still
viewed as outweighing the harms, the balance of benefits and

harms is more difficult to judge, or either the benefits or the
harms may be less clear. With a suggestion, patient values and
preferences may be more variable, and this can influence the
clinical decision that is ultimately made. Each guideline
statement also has an associated rating for the strength of
supporting research evidence. Three ratings are used: high,
moderate, or low (denoted by the letters A, B, and C, re-
spectively) and reflect the level of confidence that the evi-
dence for a guideline statement reflects a true effect based on
consistency of findings across studies, directness of the effect
on a specific health outcome, precision of the estimate of
effect,andriskofbiasinavailablestudies(AHRQ2014;Guyatt
et al., 2006; Balshem et al., 2011).

GUIDELINE STATEMENTS

Assessment and Determination of Treatment Goals

1. APA recommends (1C) that the initial psychiatric evalua-
tion of a patient with suspected alcohol use disorder in-
clude assessment of current and past use of tobacco and
alcoholaswellasanymisuseofothersubstances,including
prescribed or over-the-counter medications or supplements.

2. APA recommends (1C) that the initial psychiatric evalua-
tion of a patient with suspected alcohol use disorder in-
clude a quantitative behavioral measure to detect the
presence of alcohol misuse and assess its severity.

3. APAsuggests(2C)thatphysiologicalbiomarkersbeusedto
identify persistently elevated levels of alcohol consump-
tion as part of the initial evaluation of patients with alcohol
use disorder or in the treatment of individuals who have an
indication for ongoing monitoring of their alcohol use.

4. APA recommends (1C) that patients be assessed for co-
occurring conditions (including substance use disorders,
other psychiatric disorders, and other medical disorders)
that may influence the selection of pharmacotherapy for
alcohol use disorder.

5. APAsuggests(2C)thattheinitialgoalsoftreatmentofalcohol
use disorder (e.g. abstinence from alcohol use, reduction or
moderationofalcohol use,otherelementsofharmreduction)
be agreed on between the patient and clinician and that this
agreement be documented in the medical record.

6. APA suggests (2C) that the initial goals of treatment of
alcohol use disorder include discussion of the patient’s
legal obligations (e.g. abstinence from alcohol use, moni-
toring of abstinence) and that this discussion be docu-
mented in the medical record.

7. APA suggests (2C) that the initial goals of treatment of
alcohol use disorder include discussion of risks to self (e.g.
physical health, occupational functioning, legal involve-
ment) and others (e.g. impaired driving) from continued
use of alcohol and that this discussion be documented in
the medical record.

8. APA recommends (1C) that patients with alcohol use dis-
order have a documented comprehensive and person-
centered treatment plan that includes evidence-based
nonpharmacological and pharmacological treatments.

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Selection of a Pharmacotherapy

9. APA recommends (1B) that naltrexone or acamprosate be
offered to patients with moderate to severe alcohol use
disorder who

• have a goal of reducing alcohol consumption or achieving
abstinence

• prefer pharmacotherapy or have not responded to non-
pharmacological treatments alone

• have no contraindications to the use of these medications

10. APA suggests (2C) that disulfiram be offered to patients
with moderate to severe alcohol use disorder who

• have a goal of achieving abstinence
• prefer disulfiram or are intolerant to or have not responded
to naltrexone and acamprosate

• are capable of understanding the risks of alcohol con-
sumption while taking disulfiram

• have no contraindications to the use of this medication

11. APA suggests (2C) that topiramate or gabapentin be of-
fered to patients with moderate to severe alcohol use
disorder who

• have a goal of reducing alcohol consumption or achieving
abstinence

• prefer topiramateorgabapentin or are intolerant to or have
not responded to naltrexone and acamprosate

• have no contraindications to the use of these medications.

Recommendations Against Use of Specific Medications

12. APA recommends (1B) that antidepressant medications
not be used for treatment of alcohol use disorder unless
there is evidence of a co-occurring disorder for which an
antidepressant is an indicated treatment.

13. APA recommends (1C) that in individuals with alcohol
use disorder, benzodiazepines not be used unless treating
acute alcohol withdrawal or unless a co-occurring dis-
order exists for which a benzodiazepine is an indicated
treatment.

14. APA recommends (1C) that for pregnant or breastfeeding
women with alcohol use disorder, pharmacological treat-
mentsnot beusedunlesstreatingacutealcohol withdrawal
with benzodiazepines or unless a co-occurring disorder
exists that warrants pharmacological treatment.

15. APA recommends (1C) that acamprosate not be used by
patients who have severe renal impairment.

16. APA recommends (1C) that for individuals with mild to
moderate renal impairment, acamprosate not be used as a
first-line treatment and, if used, the dose of acamprosate
be reduced compared with recommended doses in indi-
viduals with normal renal function.

17. APA recommends (1C) that naltrexone not be used by
patients who have acute hepatitis or hepatic failure.

18. APA recommends (1C) that naltrexone not be used
as a treatment for alcohol use disorder by individu-
als who use opioids or who have an anticipated need for
opioids.

Treatment of Alcohol Use Disorder and Co-occurring
Opioid Use Disorder

19. APA recommends (1C) that in patients with alcohol use
disorderandco-occurring opioid usedisorder,naltrexone
be prescribed to individuals who

• wish to abstain from opioid use and either abstain from or
reduce alcohol use and

• are able to abstain from opioid use for a clinically appro-
priate time prior to naltrexone initiation.

GUIDELINE SCOPE

The Agency for Healthcare Research and Quality (AHRQ)
undertook a systematic review of AUD pharmacotherapy in
outpatients (Jonas et al., 2014), which serves as the foun-
dation of the systematic review for this practice guideline.
The specific medications that are discussed in the guide-
line include: acamprosate, naltrexone, disulfiram, gabapentin,
and topiramate. The guideline does not apply to the use of
these same medications for indications other than AUD. It
also does not address the management of individuals who are
intoxicated with alcohol, who require pharmacotherapy for
the acute treatment of alcohol withdrawal, or who are ex-
periencing other acute medical problems related to alcohol
use. Evidence-based psychotherapeutic treatments for AUD,
including cognitive-behavioral therapy, twelve-step facili-
tation, and motivational enhancement therapy (Anton et al.,
2006; Martin and Rehm, 2012, Project MATCH Research
Group, 1998), also play a major role in the treatment of AUD,
but specific recommendations related to these modalities are
outside the scope of this guideline.

EVIDENCE OF BENEFITS AND HARMS OF
PHARMACOTHERAPY FOR AUD

Naltrexone and acamprosate have the best available research
evidence as pharmacotherapy for patients with AUD. The
potential benefit of each medication was viewed as far out-
weighing the harms of treatment or the harms of continued
alcohol use, particularly when nonpharmacological approaches
have not produced an effect or when patients prefer to use
one of these medications as an initial treatment option. Ac-
cordingly, APA recommends (Statement 9) that that these
medications be offered to patients with moderate to severe
alcohol use disorder in specific clinical circumstances. Both
naltrexone and acamprosate have positive effects overall al-
though not all studies or outcomes show a statistically significant
benefit from these medications. Acamprosate is associated
with a small benefit on the outcomes of returning to any
drinking and number of drinking days (moderate strength
of research evidence). Naltrexone is associated with a small
benefit on the outcomes of returning to any drinking, returning
to heavy drinking, frequency of drinking days, and frequency of
heavy drinking days (moderate strength of research evidence).
In the AHRQ meta-analysis of head-to-head comparisons,

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neither acamprosate nor naltrexone showed superiority to the
other medication in terms of return to heavy drinking (mod-
erate strength of research evidence), return to any drinking
(moderate strength of research evidence), or percentage of
drinking days (low strength of research evidence). However,
in the U.S. COMBINE study (but not the German PREDICT
study), naltrexone was associated with better outcomes than
acamprosate.

For both acamprosate and naltrexone, the harms of treat-
ment are considered minimal, particularly compared with the
harms of continued alcohol use, as long as there is no con-
traindicationtotheuseofthemedication(e.g.pregnancy,renal
impairment for acamprosate, acute hepatitis/hepatic failure
fornaltrexone).Harmsofacamprosatearesmallinmagnitude,
with slight overall increases in diarrhea and vomiting as
compared with placebo (moderate strength of research
evidence). Harms of naltrexone are also small in magni-
tude, with slight overall increases in dizziness, nausea, and
vomiting relative to placebo (moderate strength of research
evidence). Alterations in hepatic function are also possible
with naltrexone. For many other potential harms, including
mortality, evidence was not available or was rated by the
AHRQ review as insufficient. However, withdrawals from
the studies due to adverse events did not differ from placebo
for acamprosate (low strength of research evidence) and
were only slightly greater than placebo for naltrexone al-
though statistically significant (moderate strength of re-
search evidence).

APA suggests (Statement 10) that disulfiram be offered
to patients with moderate to severe alcohol use disorder
in specific clinical circumstances. Although the bulk of the
research evidence for benefits and harms of disulfiram was
from randomized open-label studies, the potential benefits of
disulfiram were viewed as likely to outweigh the harms for
most patients given the medium to large effect size for the
benefit of disulfiram and particularly compared with the
harms of continued alcohol use. With carefully selected
patients in clinical trials, adverse events (e.g. drowsiness,
increased levels of hepatic enzymes, drug-drug reactions)
were somewhat greater with disulfiram. However, serious
adverse events were few and comparable in numbers to
serious adverse events in comparison groups consistent
with the long history of safe use of disulfiram in clinical
practice.

Topiramate and gabapentin are also suggested as med-
ications to be offered to patients with moderate to severe
alcohol use disorder in specific clinical circumstances (State-
ment 11). It was noted that even small effect sizes for these
medications may be clinically meaningful because of the
significant morbidity associated with AUD. A moderate
strength of research evidence from multiple randomized
controlled trials showed moderate benefit of topiramate
on drinks per drinking day, percentage of heavy drinking
days, and percentage of drinking days. Despite the bene-
fits, adverse events such as an increased likelihood of cognitive
dysfunction, dizziness, taste abnormalities, and decreased

appetite or weight loss were also reported more often with
topiramate in placebo-controlled trials in AUD.

Gabapentin was associated with moderate benefit on
rates of abstinence from drinking and abstinence from heavy
drinking(lowstrengthofresearchevidence).Gabapentinwas
not associated with an increased likelihood of adverse events
relativetoplacebo(lowstrengthofresearchevidence);however,
in studies that examined side effects of the medication in other
conditions, side effects are typically mild and have included
dizziness and somnolence. Although gabapentin had a small
positive effect, the harm of treatment was seen as being minimal,
particularly compared with the harms of continued alcohol
use, as long as there was no contraindication to the use of the
medication (e.g. pregnancy).

The full text of the practice guideline includes a detailed
description of research evidence related to effects of medi-
cation in individuals with AUD. It also describes aspects of
guideline implementation that are relevant to individual pa-
tients’ circumstances and preferences.

AUTHOR AND ARTICLE INFORMATION

From the APA Practice Guideline Writing Group (Victor I. Reus, M.D., Chair)

Address correspondence to Jennifer Medicus ([email protected]).

APA wishes to acknowledge the contributions of APA staff (Jennifer
Medicus, Seung-Hee Hong, Samantha Shugarman, Michelle Dirst, Kristin
Kroeger Ptakowski). APA and the Guideline Writing Group especially thank
Laura J. Fochtmann, M.D., M.B.I., Jeremy Kidd, M.D., Seung-Hee Hong,
and Jennifer Medicus for their outstanding work and effort on developing
this guideline. APA also thanks the APA Steering Committee on Practice
Guidelines (Michael Vergare, M.D., Chair), liaisons from the APA Assembly
for their input and assistance, and APA Councils and others for providing
feedback during the comment period.

Am J Psychiatry 2018; 175:86–90; doi: 10.1176/appi.ajp.2017.1750101

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fpsyg-08-00884 May 25, 2017 Time: 12:13 # 1

PERSPECTIVE
published: 29 May 2017

doi: 10.3389/fpsyg.2017.00884

Edited by:
Bernhard Hommel,

Leiden University, Netherlands

Reviewed by:
Reinout W. Wiers,

University of Amsterdam, Netherlands
Thomas Edward Gladwin,

Ministry of Defense, Netherlands

*Correspondence:
Ann-Kathrin Stock

[email protected]
dresden.de

Specialty section:
This article was submitted to

Cognition,
a section of the journal
Frontiers in Psychology

Received: 28 February 2017
Accepted: 15 May 2017
Published: 29 May 2017

Citation:
Stock A-K (2017) Barking up

the Wrong Tree: Why and How We
May Need to Revise Alcohol

Addiction Therapy.
Front. Psychol. 8:884.

doi: 10.3389/fpsyg.2017.00884

Barking up the Wrong Tree: Why
and How We May Need to Revise
Alcohol Addiction Therapy
Ann-Kathrin Stock*

Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität
Dresden, Dresden, Germany

One of the main characteristics of alcohol abuse and addiction is the loss of control over
alcohol intake and the continuation of drinking in the face of negative consequences.
Mounting evidence strongly suggests that an alcohol-induced imbalance between goal-
directed and habitual behavior may be one of the main driving factors of this key feature
of addiction and furthermore play a key role in staying abstinent. Current therapies often
focus only on deficient inhibitory control (i.e., goal-directed behavior), but largely neglect
the potential of the well-functioning habit formation found in patients. Yet, focusing on
intact habitual/automatic mechanisms in addition to or maybe even instead of deficient
cognitive control might equip us with a more effective tool to battle the current alcohol
abuse and addiction epidemic, especially with respect to more severely impacted
patients who likely suffer from permanent alcohol-induced brain damage. Against this
background, I would like to advocate the application and scientific evaluation of habit
reversal therapy (HRT) for alcohol abuse and addiction.

Keywords: alcohol, addiction, AUD, control, habit reversal therapy, inhibition, therapy

INTRODUCTION

In many, if not most parts of the world, alcohol abuse and addiction are problems of epidemic
proportions which do not only cause a wide range of health problems for the affected individuals,
but also skyrocketing costs for healthcare systems as well as a great number of socioeconomic
problems (WHO, 2016). Considerable efforts are made to alleviate the adverse effects of this
epidemic, but most countries have not had noteworthy decreases in alcohol consumption per
capita within the last 25 years1. Also, relapse rates among alcohol use disorder (AUD) patients have
remained extremely high across different currently used therapeutic approaches, with often more
than 50% 2 of patients consuming again after completing therapy (Garbusow et al., 2014; Naqvi
and Morgenstern, 2015). Against this background, we are in dire need of better treatment options.

1 http://www.who.int/gho/alcohol/consumption_levels/adult_recorded_percapita/en/
2 For example, Jason et al. (2006) reported that 65% of n = 75 alcohol addicts receiving “usual after-care” relapsed within
24 months after detoxification. Picci et al. (2014) found that 47% of n = 168 alcohol-dependent patients had already relapsed
6 months after their detoxification in a hospital. Kolla et al. (2015) reported relapse rates of 48% in patients undergoing
treatment and 66% ITT in alcohol-dependent patients within 12 months after treatment (n = 119). Nalpas et al. (2003)
reported that the mean time until the first relapse in n = 267 patients addicted to alcohol was between 3.37 and 5.89 months
in four different treatment centers.
As relapse rates are cumulative measures, those numbers rise even further when looking at longer follow-up time spans. For
example, Garbusow et al. (2014) stated in their review that “on the average, 8 or 9 out of 10 alcohol-dependent patients relapse
after detoxification,” but they did not provide a time span.

Frontiers in Psychology | www.frontiersin.org 1 May 2017 | Volume 8 | Article 884

fpsyg-08-00884 May 25, 2017 Time: 12:13 # 2

Stock Why Revise Alcohol Addiction Therapy?

One of the possible reasons for the low success rates of current
AUD treatments is that even though the last decades have seen
an unprecedented surge in alcohol abuse and addiction research,
many clinical therapeutic approaches do not (yet) consider the
latest findings. And while this is not the case for evidence-
based treatments, it has recently been noted that even those are
currently only modestly effective (Naqvi and Morgenstern, 2015).

In order to improve the current situation, effective therapeutic
interventions need to be rooted in a mechanistic, not
just a correlational, understanding of the behavioral and
neurobiological changes that cause harmful consumption and
lead to relapse in AUD patients. Based on advances in basic
cognitive neuroscience research on the effects of alcohol on the
nervous system and behavior, we might now be able to rise to
this challenge. In the light of accumulating evidence that alcohol
seems to shift the healthy balance between goal-directed and
habitual behavior towards the latter, it appears that we might have
been barking up the wrong tree all along: Mainly focusing on
the cognitive control deficits observed in AUDs, we have utterly
neglected cognitive functions such as habits and automatisms,
which have lately been proven to remain largely preserved.
This is quite unfortunate as preserved cognitive functions
provide promising working points to establish alternatives or
additions to currently popular therapeutic approaches. As this
potential opportunity might benefit millions of patients, current
approaches and potential alternatives will be contrasted and
discussed in the following.

ALCOHOL AND CONTROL DEFICITS

One of the key problems contributing to relapses in AUD patients
are executive control deficits which result in the inability to
control alcohol intake and lead a productive, self-serving life
(Fein and Cardenas, 2015; Koob and Volkow, 2016). The term
‘executive control’ subsumes several cognitive functions that help
us to adapt to new situations, solve problems, and, perhaps
most importantly, counteract impulsive or automatic behavior
(for a detailed review, see Diamond, 2013). Among all executive
functions, inhibitory control plays a special role in alcohol abuse
(Copersino, 2017). It is defined as our ability to control thoughts,
emotions, attention and behavior in order to resist temptations,
internal predispositions or habits and replace them with more
appropriate, goal-directed behavior (Diamond, 2013). Being able
to control habits is key to maintaining abstinence as habitual
actions substantially contribute to addiction (McKim et al., 2016).
In the early stages of substance (ab)use, the consumption of
alcohol is usually motivated by the reinforcing hedonic effects
of alcohol, but probably due to an interaction of pavlovian and
instrumental learning, repeated self-administration gradually
shifts the mechanisms driving behavior to stimulus-response
(S-R) associations. This eventually leads to the formation
of habits and compulsions which are no longer sensitive
to outcome devaluation (Everitt and Robbins, 2005; de Wit
and Dickinson, 2009; McKim et al., 2016) because alcohol
consumption is no longer driven by expected outcomes, but
instead triggered by alcohol-associated stimuli (de Wit and

Dickinson, 2009; McKim et al., 2016).3 In practical terms,
this behavioral autonomy means that AUD patients tend
to maintain their alcohol consumption even in the face of
negative consequences, which contributes to the development
and maintenance of addictive behavior in AUD (Corbit and
Janak, 2016; López et al., 2016). Of note, this effect extends
to other behavioral domains as well since alcohol has been
shown to shift even consumption-unrelated behavior from
goal-directed towards habit-based processes (e.g., Stock et al.,
2016) and to generally reduce goal-directed executive control
capacities including behavioral inhibition (Brion et al., 2014;
Garbusow et al., 2014; Day et al., 2015; Fein and Cardenas, 2015;
Trantham-Davidson and Chandler, 2015; Koob and Volkow,
2016).4

Altogether, these changes result in a dysfunctional state
where behavioral control is reduced, while the automatisms it
should keep in check prevail or may even become enhanced
over the course of an AUD (see Figure 1 for illustration).
Based on this lack of control capacities, it may seem like
the most logical consequence to try to enhance executive
functioning/cognitive control in AUD patients (Verdejo-Garcia,
2016), who may present with sometimes severe impairments
of this cognitive domain and therefore fail to abstain from
drinking (Harper, 2007; Brion et al., 2014). In line with this
approach, it has been shown that cognitive control training
like goal management training (GMT) may improve executive
functions in individuals with substance use disorders (Alfonso
et al., 2011). Furthermore, cognitive control training seems to
have mildly beneficial effects on the alcohol consumption of
non-addicted heavy drinkers (Berg, 1948) and individuals with
hazardous drinking behavior who reported relatively strong
automatic preferences for alcohol (Houben et al., 2011). Yet
still, it is questionable whether more severely impaired AUD
patients who already suffer from alcohol-related brain damage
and/or Korsakoff ’s syndrome (KS) are able to benefit from
cognitive control training. The reason for this assumption
is that severe alcohol abuse and the resulting thiamine
deficiency often lead to brain damage including thalamic or
frontal cortical atrophy (Brun and Andersson, 2001; Oscar-
Berman et al., 2004; Matsumoto, 2009; Oscar-Berman, 2012;
Maharasingam et al., 2013; Pitel et al., 2015) as well as functional
changes within fronto-striatal loops and the dopaminergic
and GABAergic transmitter systems (Everitt and Robbins,
2005; Gremel and Costa, 2013; Sjoerds et al., 2013; Barker
et al., 2015; Koob and Volkow, 2016; Gremel and Lovinger,
2017), all of which may which cause severe executive control

3 The underlying mechanisms can be explains within the framework of several
dual-process theories. For example, the associative cybernetic model by de Wit
and Dickinson (2009) suggests a mechanistic account of how alcohol abuse may
lead to behavioral autonomy based on changes in outcome-response associations
and response-outcome associations. In contrast to this, the Rł model suggests
a continuum of automatic versus reflective processing where response selection
depends on how much evaluation precedes selection (Gladwin et al., 2011, 2017).
4 Importantly, the imbalance of habitual and goal-directed control behavior is not
only present in AUD patients (Sebold et al., 2014), but can also be found in animal
models (Corbit and Janak, 2016) and healthy humans during high-dose alcohol
intoxication (Stock et al., 2014, 2016), which suggests that alcohol is a causal factor
for this imbalance.

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FIGURE 1 | Alcohol impairs the balance between goal-directed and habitual behavior so that habitual behavior like compulsive drinking can no longer be kept in
check by goal-directed control mechanisms such as inhibition. Many conventional therapies primarily aim at improving/augmenting goal-directed cognitive control so
that habitual drinking can be overcome. Unfortunately, alcohol abuse may permanently damage frontal brain areas and thus diminish control faculties so that quite a
few AUD patients may never develop goal-directed behavior that can effectively keep their drinking habits in check. Against this background, I would like to espouse
alternative therapeutic approaches like HRT which aim at modifying or changing habits instead of trying to inhibit them via goal-directed behavior (for details, please
see Habit-Based Treatment Options).

deficits that do not necessarily seem to fully to recover with
abstinence (Thomson, 2000; Harper, 2007; Trantham-Davidson
and Chandler, 2015). And as consciously controlling habitual
drinking heavily strains cognitive control capacities, this means
that cognitive control training and related standard addiction
treatments may only benefit patients in early stages of AUD
who have not yet suffered substantial damage to the brain areas
mediating this cognitive faculty (Copersino, 2017; Gladwin et al.,
2017).

HABIT-BASED TREATMENT OPTIONS

At this point, the outlook for patients with marked control
deficits may seem bleak, but instead of focusing on potentially
irreversibly damaged control capacities, one could also try
to find a working point by focusing on relatively preserved
cognitive functions. As previously noted, habits and automatisms
seem to be rather unimpaired by alcohol abuse. So far, there
has been comparatively little research on this therapeutic
potential, but a few studies based on a retraining of automatic
approach/avoidance tendencies towards alcohol-related
stimuli have provided first hints for the efficacy of such
interventions (Wiers et al., 2011; Naqvi and Morgenstern,
2015).

In this context, the probably best-known approach to altering
unwanted AUD behavior via habits and automatisms (instead
of addressing cognitive control), is cognitive bias modification
(CBM) (for an overview, see Gladwin et al., 2017). Put simply,
it is based on the aforementioned finding of S-R-driven alcohol
consumption and the observation that untreated AUD patients
show an automatic approach bias, which seems to be reduced in
patients who benefit from AUD therapy (Gladwin et al., 2017).
Based thereon, CBM aims to establish an automatic avoidance

tendency towards alcohol-related stimuli, which is often done by
asking patients to push a lever or joystick towards visual stimuli
like pictures of soft drinks (or other non-alcohol stimuli) while
pulling it away from alcohol-related stimuli (Wiers et al., 2011;
Eberl et al., 2013; Boendermaker et al., 2016; Gladwin et al., 2017).
In addition to this response-targeted CBM, the same research
group has also investigated attentional bias modification (ABM)
procedures aimed at reducing the amount of attention allocated
to alcohol-related stimuli, but clinical effects of the latter still
remain to be established. Response-based CBM has been shown
to generalize to untrained visual stimuli (Wiers et al., 2011) and
to reduce relapse rates in AUD patients without KS after 1 year
(49.8% of n = 248 with CBM vs. 57.3% of n = 227 without) when
used as an add-on to regular AUD therapy (Eberl et al., 2013).

But even with CBM, roughly half of the treated patients still
experience relapses and not all studies using CBM are able to
find a clear-cut beneficial effect on relapse rates (Wiers et al.,
2015; Copersino, 2017). While a lack of motivation in some of the
participants may have contributed to this (Gladwin et al., 2017), it
is also conceivable that the reason for this lies in the specificity of
the treatment as CBM targets only one specific aspect of habitual
responding out of the wide range of S-R associations and the
resulting addiction behavior in AUD.

This is why I would like to advocate for a well-known way of
altering habits which is more comprehensive, but has so far not
been applied to AUDs: the habit reversal therapy (HRT).

Habit reversal therapy encompasses several stages designed to
alter dysfunctional habits without heavily relying on executive
control and has already been proven to be effective in
other disorders characterized by unwanted automatisms/habitual
behavior, such as Tourette syndrome and chronic tic disorder or
trichotillimania (Snorrason et al., 2015; Whittington et al., 2016;
Yang et al., 2016).

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During the initial “awareness training,” the patient is made
aware of his/her habits and automatisms. In the case of AUD
patients, this should probably include several aspects of their S-R
association-based habitual alcohol consumption. To my mind,
this should include identifying stimuli and situations triggering
addictive behavior and also put a major focus on the different
(chains of ) responses constituting the habit.

In a subsequent therapy phase (“development of a competing
response”), an effective competing habit or response, which
needs to be carried out every time the urge to perform the
initial unwanted habit/automatism emerges, is developed (in
case specific behavior-eliciting stimuli have been identified
during the initial phase, this would, however, also apply to
situations where such stimuli are encountered – irrespective
of whether they elicit craving). In this phase, the unwanted
automatic behavior becomes altered or replaced by another
habit/automatism which is established as part of the therapy. This
is crucial as the approach requires only little cognitive control
to effectively alter behavior in the long run. Importantly, this
means that any irreversible cognitive control deficits that may
have resulted from former alcohol abuse are not as much of an
impediment to the success of this therapeutic intervention as
it would have been in many alternative therapeutic approaches.
In case of less complex unwanted automatisms such as tics,
patients are often trained to develop a habit of performing
a counteractive motor movement and the aforementioned
CBM training already does something closely related by
trying to counteract automatic alcohol approach tendencies
by establishing competing avoidance responses. However, it
has been recognized as a problem for standardized retraining
strategies such as CBM that the range of S-R associations and
implicit responses in AUD is far beyond the scope of this
approach/avoidance aspect and substantially increases over the
course of addiction (Copersino, 2017). In case the individual
responses culminating in harmful alcohol consumption are
carefully analyzed and dissected during the initial “awareness
training” phase, it should, however, become possible to develop
specific, individually tailored counteractive habits to several of
these responses (like routinely doing sport after every frustrating
event or day, screwing the lid of a bottle shut instead of
opening it, or pouring alcohol into the sink instead of into
a glass, just to make up a few examples5). The establishment
of competing responses is further promoted by the therapy
building block “generalization of new skills” that helps to
generalize the competing response to as many relevant contexts
as possible/necessary (which is something has mostly been
neglected in previous therapeutic approaches). As a consequence,
conscious and effortful controls are required less and less over
time. Lastly, the block of “building motivation” is designed to
motivate the patients to keep up with therapy (again without
requiring too much top-down behavioral control). This aspect
of HRT is important to maintain the patients’ compliance as the
development and generalization of competing responses takes

5 These examples are just meant to provide a rough mechanistic idea as the
development of effective competing responses likely requires an individually
tailored approach (which cannot be illustrated in detail without a case study) and
extensive therapeutic experience.

time and therefore does not yield immediate effects/rewards.
Also, adequate motivation as well as the development of a
positive long-term perspective seem to be a crucial prerequisite
to yield positive outcomes when trying to manipulate automatic
processes in AUD (Gladwin et al., 2017).6

While the development of competing habits/responses would
certainly require an individually tailored approach for each
patient, it holds the potential of breaking chains of responses
that would otherwise culminate in alcohol consumption.
Based thereon, HRT (probably also in combination with
CBM) might provide an exciting new therapy option for
AUD patients with severe and/or permanent executive control
deficits who cannot sufficiently benefit from cognitive control
training.

CONCLUSION

Since HRT has not yet been applied and evaluated in the
context of alcohol addiction, more research is needed to
establish whether it provides an effective addition to or
even replacement of control-focused AUD therapy. Also, we
need to put further consideration into how potent competing
responses can be developed. Importantly, this perspective does
in no way intend to disregard the fact that the imbalance
between habitual and goal-directed behavior is by far not the
only mechanism at work in AUD, or that pharmacological
interventions may provide valuable support for therapeutic
advances. Yet, accumulating evidence strongly suggests that the
alcohol-induced imbalance between goal-directed and habitual
behavior may play a key role in staying abstinent. Hence,
focusing on intact habitual/automatic mechanisms in addition
to or maybe even instead of deficient cognitive control
might equip us with a more effective tool to battle the
current alcohol abuse and addiction epidemic, especially with
respect to more severely impacted patients who likely suffer
from permanent alcohol-induced brain damage. Against this
background, I would like to advocate the application and
scientific evaluation of HRT or similar therapies for alcohol abuse
and addiction.

AUTHOR CONTRIBUTIONS

The author confirms being the sole contributor of this work and
approved it for publication.

FUNDING

This work was funded by a grant from the “Deutsche
Forschungsgemeinschaft” (DFG) SFB940-B8 to A-KS.

6 One of the reasons for this could be that non-conscious cognitive and
motivational processes are responsible for the effects of mental contrasting, which
may produce either active goal pursuit or active goal disengagement, depending on
the expectation of success (Oettingen, 2012).

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How to Write a Diagnosis According to the DSM-5

An Aid for MSW Students

As you write a diagnosis, keep in mind that “[there] are specific recording protocols for
these diagnostic codes…to insure consistent, international recording” (American
Psychiatric Association, 2013, p. 23).

Writing a Diagnosis
A diagnosis is written as a simple list in order of priority to the current treatment needs.

F33.1 Major depressive disorder, moderate, recurrent, with seasonal pattern
F41.1 Generalized anxiety disorder
Z60.3 Acculturation difficulty

Each diagnosis needs an ICD code that is written before the name of the diagnosis.
The older (DSM-IV-TR) names of some disorders can sometimes be found after the
current name. However, to avoid confusion, only use the current name for the illness in
a diagnosis.

ICD Codes

The DSM-5 includes codes for the International Classification of Diseases. Both ICD-9
and ICD-10 are included in the DSM-5. Always ignore the ICD-9 codes and use only
the ICD-10-CM codes in diagnosis.

The ICD-10-CM codes are listed inside the parentheses in the screen shot below.

HOW TO CODE

For mental health conditions, codes always start with a letter (usually F), followed by 2–
6 digits. A code is not valid unless it has been coded to the full number of digits
required. A code with only the first three digits is used only if that condition is not further
subdivided within the DSM-5.

For example, for schizophrenia, there are no additional characters in spaces 4, 5, 6, and
7.

F20.9 Schizophrenia

In other cases, numbers must be added in the 4th, 5th, or 6th spaces to individualize a
condition. Spaces 4–6 provide greater detail of causes, location details, and severity.
For example, here are two codes for mania:

F30.10 Manic episode without psychotic symptoms, unspecified

F30.11 Manic episode without psychotic symptoms, mild 


Many disorders have more than one ICD code when there are common, clearly
identified subtypes to the illness. The diagnostic criteria box always tells you if a
code must be subdivided.

If you do not see a code at the top of the diagnostic criteria box, look for the correct
codes at the bottom of the box. Often the box prompts for further individualization by
saying “Specify if” or “Specify whether.” You may also be asked to set a severity level.

The wording “specify whether” tells you that the subtypes that follow are mutually
exclusive.

For example, here are two subtypes for schizoaffective disorder:

F25.0 Schizoaffective disorder, bipolar type
F25.1 Schizoaffective disorder, depressive type

Always check for coding notes for further directions. For example, in addition to our
subtypes for schizoaffective disorder, if catatonia is present, an additional code is found
in the coding note.

Now our diagnosis looks like this:

F25.0 Schizoaffective disorder, bipolar type
F06.1 Catatonia (associated with another mental disorder)

After the subtype for schizoaffective disorder is identified, the diagnostic box requires
even more individualization: “Specify if” is followed by “Specify current severity.”
These terms prompt the clinician to further detail the course of the illness and the way to
measure the severity of a presentation.

F25.0 Schizoaffective disorder, bipolar type, multiple episodes, currently in
acute episode, symptom severity
F06.1 Catatonia (associated with another mental disorder)

Some disorders such as the substance/medication-induced disorders have more
complex codes for their subtypes. When this happens, there is always a table and a
coding note found at the bottom of the diagnostic criteria box.

Be aware that some diagnoses use the same code because the ICD has limitations that
are already being updated for ICD-11. Always check the Centers for Medicare and

Medicaid Services (CMS) and the National Center for Health Statistics for updated
coding on those disorders that share a code.

HOW TO LIST MULTIPLE CODES

Formal DSM-5 diagnosis combines into one list all relevant mental disorders, including
personality disorders, disabilities, and other relevant medical diagnoses. The DSM-5
also expands the psychosocial stressors that a patient might be experiencing. These
are now called “other conditions that are a focus of treatment,” and most of them
begin with the letter “Z.” These conditions, which are critical to psychosocial
treatment (formerly known as the V codes), are found on p. 715 in the manual.

In a diagnostic list, always place the principal diagnosis first (the reason for the visit,
if in an outpatient setting). Other mental health co-morbid diagnoses follow in order of
priority to the treatment or focus of attention.

1. RULE A: In this diagnostic list, a mental disorder was the reason for the visit, with
the client experiencing an additional medical condition unrelated to the mental
disorder diagnosis. Other psychosocial factors relevant to the service are listed
after mental health conditions and physical conditions:

F40.00 Agoraphobia
K7030 Alcoholic cirrhosis of liver without ascites (by patient report)
Z60.3 Acculturation difficulty
Z72.0 Tobacco use disorder, mild (nicotine use)

The order of priority above is (a) principal mental health diagnosis, (b) medical
factors, and (c) psychosocial needs.

2. RULE B: If the client above has a clinical diagnosis of a mental health problem as

the principal diagnosis (all F codes), with the presence of a second, additional
mental disorder but without the medical problem of cirrhosis, the diagnosis looks
like this:

F40.00 Agoraphobia
F50.01 Anorexia nervosa, restricting subtype
Z60.3 Acculturation difficulty.
Z72.0 Tobacco use disorder, mild (nicotine use)

3. RULE C: An exception to rules A and B occurs only when the “other medical
condition” is thought to be causing the mental disorder. In such cases, the
medical condition should be listed first. Here, damage to the liver is also causing
a neurocognitive disorder.

K7030 Alcoholic cirrhosis of liver without ascites
F10.988 Mild neurocognitive disorder, without alcohol use

Z60.3 Acculturation difficulty
Z72.0 Tobacco use disorder, mild (nicotine use)

OTHER CONVENTIONS

In diagnosis, a clinician must first rule out if the condition is being caused by a physical
illness, then if it is caused by a substance use problem, and only then are mental
disorders investigated.

A diagnosis should only be provided once a comprehensive assessment has been
completed. The DSM-5 has online assessment measures to help in diagnosis.

In older diagnostics, clinicians used “diagnosis deferred” (799.9 in ICD-9) when they
were not ready to assign a diagnosis. There is no analogous code in the ICD-10;
instead, a clinician should use “provisional” or “other specified disorder,” when
appropriate.

A provisional diagnosis is preferred for mental health conditions, if the reason for
delaying diagnosis is that sufficient criteria to meet diagnostic category is not
documentable because of limited assessment. The APA (2013) tells clinicians to use a
provisional diagnosis “when you have a strong ‘presumption’ that the full criteria will
ultimately be met for a disorder but not enough information is available to make a firm
diagnosis” (p. 23). The word provisional simply follows the full diagnostic label:

F40.00 Agoraphobia, provisional

When symptoms are present but do not meet all the criteria needed for a diagnosis,
such as when symptoms are mixed or below the diagnostic threshold but are causing
significant distress, most chapters in the DSM-5 have an “Other Specified Disorder”
category. If used, the clinician then specifies the presentation according to specifiers
provided in the diagnostic box. For example, there are several options for F28 Other
Specified Schizophrenia Spectrum and Other Psychotic Disorder, one of example of
which is shown below:

F28 Other specified schizophrenia spectrum disorder, persistent
auditory hallucinations

While each chapter in the DSM-5 has an “UNSPECIFIED” code, clinicians are asked
not to use this in routine treatment situations. Insurance carriers have variable rules
about this label. The CMS actually designed the term for situations in which there is
insufficient information to make a diagnosis—for example, in settings like emergency
rooms. If you are using “UNSPECIFIED,” be prepared for many insurance carriers to
deny services and payments on the basis that there is no “medical necessity” present.

While all social workers need to know how to read and interpret diagnoses, state laws
determine if you can provide a direct diagnosis yourself. In most states, Licensed
Clinical Social Workers do assess and diagnose. Please look up your state laws.

References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental

disorders (5th ed.). Arlington, VA: Author.

American Psychiatric Association. (2018). DSM–5 frequently asked questions.

Retrieved from https://www.psychiatry.org/psychiatrists/practice/dsm/feedback-
and-questions/frequently-asked-questions

Centers for Disease Control and Prevention. (2017a). ICD-10-CM official guidelines for

coding and reporting: FY 2017 (October 1, 2016–September 30, 2017).
Retrieved from http://www.cdc.gov/nchs/data/icd/10cmguidelines_2017_final.pdf

Centers for Disease Control and Prevention. (2017b). International classification of

diseases, tenth revision, clinical modification (ICD-10-CM). Retrieved from
https://www.cdc.gov/nchs/icd/icd10cm.htm

Centers for Medicare and Medicaid Services. (2017). Provider resources. Retrieved

from https://www.cms.gov/Medicare/Coding/ICD10/ProviderResources.html

Material in this guide has been adapted from the referenced materials by Dr. Diane H.
Ranes, PhD, LCSW.

Also from James Morrison

Diagnosis Made Easier:
Principles and Techniques for Mental Health Clinicians, Second Edition

The First Interview, Fourth Edition

When Psychological Problems Mask Medical Disorders:

A Guide for Psychotherapists

For more information, see www.guilford.com/morrison

2

DSM-5® Made Easy
The Clinician’s Guide to Diagnosis

James Morrison

THE GUILFORD PRESS
New York London

3

Epub Edition ISBN: 9781462515448; Kindle Edition ISBN: 9781462515455

© 2014 The Guilford Press
A Division of Guilford Publications, Inc.
72 Spring Street, New York, NY 10012
www.guilford.com

All rights reserved

No part of this book may be reproduced, translated, stored in a retrieval system, or transmitted, in any form
or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without
written permission from the publisher.

Last digit is print number: 9 8 7 6 5 4 3 2 1

The author has checked with sources believed to be reliable in his effort to provide information that is
complete and generally in accord with the standards of practice that are accepted at the time of publication.
However, in view of the possibility of human error or changes in behavioral, mental health, or medical
sciences, neither the author, nor the editor and publisher, nor any other party who has been involved in the
preparation or publication of this work warrants that the information contained herein is in every respect
accurate or complete, and they are not responsible for any errors or omissions or the results obtained from
the use of such information. Readers are encouraged to confirm the information contained in this book with
other sources.

Library of Congress Cataloging-in-Publication Data

Morrison, James R., author.
DSM-5 made easy : the clinician’s guide to diagnosis / James Morrison.
p.; cm.
Includes bibliographical references and index.
ISBN 978-1-4625-1442-7 (hardcover : alk. paper)
I. Title.
[DNLM: 1. Diagnostic and statistical manual of mental disorders. 5th ed 2. Mental Disorders—

diagnosis—Case Reports. 3. Mental Disorders—classification—Case Reports. WM 141]
RC469
616.89’075—dc23

2014001109

DSM-5 is a registered trademark of the American Psychiatric Association. The APA has not participated in
the preparation of this book.

4

For Mary, still my sine qua non

5

About the Author

James Morrison, MD, is Affiliate Professor of Psychiatry at Oregon Health and
Science University in Portland. He has extensive experience in both the private
and public sectors. With his acclaimed practical books—including, most recently,
Diagnosis Made Easier, Second Edition, and The First Interview, Fourth Edition—
Dr. Morrison has guided hundreds of thousands of mental health professionals
and students through the complexities of clinical evaluation and diagnosis. His
website (www.guilford.com/jm) offers additional discussion and resources related
to psychiatric diagnosis and DSM-5.

6

Acknowledgments

Many people helped in the creation of this book. I want especially to thank my
wife, Mary, who has provided unfailingly excellent advice and continual support.
Chris Fesler was unsparing with his assistance in organizing my web page.

Others who read portions of the earlier version of this book, DSM-IV Made
Easy, in one stage or another included Richard Maddock, MD, Nicholas
Rosenlicht, MD, James Picano, PhD, K. H. Blacker, MD, and Irwin Feinberg,
MD. I am grateful to Molly Mullikin, the perfect secretary, who contributed
hours of transcription and years of intelligent service in creating the earlier
version of this book. I am also profoundly indebted to the anonymous reviewers
who provided input; you know who you are, even if I don’t.

My editor, Kitty Moore, a keen and wonderful critic, helped develop the
concept originally, and has been a mainstay of the enterprise for this new edition.
I also deeply appreciate the many other editors and production people at The
Guilford Press, notably Editorial Project Manager Anna Brackett, who helped
shape and speed this book into print. I would single out Marie Sprayberry, who
went the last mile with her thoughtful, meticulous copyediting. David Mitchell
did yeoman service in reading the manuscript from cover to cover to root out
errors. I am indebted to Ashley Ortiz for her intelligent criticism of my web page,
and to Kyala Shea, who helped get it web borne.

A number of clinicians and other professionals provided their helpful advice in
the final revision process. They include Alison Beale, Ray Blanchard, PhD, Dan
G. Blazer, MD, PhD, William T. Carpenter, MD, Thomas J. Crowley, MD,
Darlene Elmore, Jan Fawcett, MD, Mary Ganguli, MD, Bob Krueger, PhD,
Kristian E. Markon, PhD, William Narrow, MD, Peter Papallo, MSW, MS,
Charles F. Reynolds, MD, Aidan Wright, PhD, and Kenneth J. Zucker, PhD. To
each of these, and to the countless patients who have provided the clinical
material for this book, I am profoundly grateful.

7

Contents

Also from James Morrison

Title Page

Copyright Page

Dedication Page

About the Author

Acknowledgments

FREQUENTLY NEEDED TABLES

INTRODUCTION

CHAPTER 1 Neurodevelopmental Disorders

CHAPTER 2 Schizophrenia Spectrum and Other Psychotic Disorders

CHAPTER 3 Mood Disorders

CHAPTER 4 Anxiety Disorders

CHAPTER 5 Obsessive–Compulsive and Related Disorders

CHAPTER 6 Trauma- and Stressor-Related Disorders

CHAPTER 7 Dissociative Disorders

CHAPTER 8 Somatic Symptom and Related Disorders

CHAPTER 9 Feeding and Eating Disorders

CHAPTER 10 Elimination Disorders

CHAPTER 11 Sleep–Wake Disorders

CHAPTER 12 Sexual Dysfunctions

CHAPTER 13 Gender Dysphoria

CHAPTER 14 Disruptive, Impulse-Control, and Conduct Disorders

CHAPTER 15 Substance-Related and Addictive Disorders

8

CHAPTER 16 Cognitive Disorders

CHAPTER 17 Personality Disorders

CHAPTER 18 Paraphilic Disorders

CHAPTER 19 Other Factors That May Need Clinical Attention

CHAPTER 20 Patients and Diagnoses

APPENDIX

Essential Tables

Global Assessment of Functioning (GAF) Scale

Physical Disorders That Affect Mental Diagnosis

Classes (or Names) of Medications That Can Cause Mental Disorders

INDEX

About Guilford Press

Discover Related Guilford Books

9

Frequently Needed Tables

TABLE 3.2 Coding for Bipolar I and Major Depressive Disorders

TABLE 3.3 Descriptors and Specifiers That Can Apply to Mood Disorders

TABLE 15.1 Symptoms of Substance Intoxication and Withdrawal

TABLE 15.2
ICD-10-CM Code Numbers for Substance Intoxication, Substance
Withdrawal, Substance Use Disorder, and Substance-Induced Mental
Disorders

TABLE 16.1 Coding for Major and Mild NCDs

Purchasers of this ebook can download copies of these tables from
www.guilford.com/morrison2-forms.

10

Introduction

The summer after my first year in medical school, I visited a friend at his home
near the mental institution where both of his parents worked. One afternoon,
walking around the vast, open campus, we fell into conversation with a staff
psychiatrist, who told us about his latest interesting patient.

She was a young woman who had been admitted a few days earlier. While
attending college nearby, she had suddenly become agitated—speaking rapidly
and rushing in a frenzy from one activity to another. After she impulsively sold
her nearly new Corvette for $500, her friends had brought her for evaluation.

“Five hundred dollars!” exclaimed the psychiatrist. “That kind of thinking,
that’s schizophrenia!”

Now my friend and I had had just enough training in psychiatry to recognize
that this young woman’s symptoms and course of illness were far more consistent
with an episode of mania than with schizophrenia. We were too young and
callow to challenge the diagnosis of the experienced clinician, but as we went on
our way, we each expressed the fervent hope that this patient’s care would be less
flawed than her assessment.

For decades, the memory of that blown diagnosis has haunted me, in part
because it is by no means unique in the annals of mental health lore. Indeed, it
wasn’t until many years later that the first diagnostic manual to include specific
criteria (DSM-III) was published. That book has since morphed into the
enormous fifth edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM-5), published by the American Psychiatric Association.

Everyone who evaluates and treats mental health patients must understand
the latest edition of what has become the world standard for evaluation and
diagnosis. But getting value from DSM-5 requires a great deal of concentration.
Written by a committee with the goal of providing standards for research as well
as clinical practice in a variety of disciplines, it covers nearly every conceivable
subject related to mental health. But you could come away from it not knowing
how the diagnostic criteria translate to a real live patient.

I wrote DSM-5 Made Easy to make mental health diagnosis more accessible to
clinicians from all mental health professions. In these pages, you will find
descriptions of every mental disorder, with emphasis on those that occur in
adults. With it, you can learn how to diagnose each one of them. With its careful

11

use, no one today would mistake that young college student’s manic symptoms
for schizophrenia.

WHAT HAVE I DONE TO MAKE DSM-5 EASY?

Quick Guides. Opening each chapter is a summary of the diagnoses addressed
therein—and other disorders that might afflict patients who complain about
similar problems. It also provides a useful index to the material in that chapter.

Introductory material. The section on each disorder starts out with a brief
description designed to orient you to the diagnosis. It includes a discussion of the
major symptoms, perhaps a little historical information, and some of the
demographics—who is likely to have this disorder, and in what circumstances.
Here, I’ve tried to state that which I would want to know myself if I were starting
out afresh as a student.

Essential Features. OK, that’s the name I’ve given them in in DSM-5 Made Easy,
but they’re also known as prototypes. I’ve used them in an effort to make the
DSM-5 criteria more accessible. For years, we working clinicians have known that
when we evaluate a new patient, we don’t grab a list of emotional and behavioral
attributes and start ticking off boxes. Rather, we compare the data we’ve gathered
to the picture we’ve formed of the various mental and behavioral disorders.
When the data fit an image, we have an “aha!” experience and pop that diagnosis
into our list of differential diagnoses. (From long experience and conversations
with countless other experienced clinicians, I can assure you that this is exactly
how it works.)

Very recently, a study of mood and anxiety disorders* has found that clinicians
who make diagnoses by rating their patients against prototypes perform at least as
well as, and sometimes better than, other clinicians who adhere to strict criteria.
That is, it can be shown that prototypes have validity even greater than that of
some DSM diagnostic criteria. Moreover, prototypes are reported to be usable by
clinicians with a relatively modest level of training and experience; you don’t have
to be coming off 20 years of clinical work to have success with prototypes. And
clinicians report that prototypes are less cumbersome and more clinically useful.
(However—and I hasten to underscore this point—the prototypes used in the
studies I have just mentioned were generated from the diagnostic criteria
inherent in the DSM criteria.) The bottom line: Sure, we need criteria, but we can
adapt them so they work better for us.

12

So once you’ve collected the data and read the prototypes, I recommend that
you assign a number to indicate how closely your patient fits the ideal of any
diagnoses you are considering. Here’s the accepted convention: 1 = little or no
match; 2 = some match (the patient has a few features of the disorder); 3 =
moderate match (there are significant, important features of the disorder); 4 =
good match (the patient meets the standard—the diagnosis applies); 5 = excellent
match (a classic case). Obviously, the vignettes I’ve provided will always match at
the 4 or 5 level (if not, why would I use them as illustrative examples?), so I
haven’t bothered to grade them on the 5-point scale. But you should do just that
with each new patient you interview.

Of course, there may be times you’ll want to turn to the official DSM-5
criteria. One is when you’re just starting out, so you can get a picture of the exact
numbers of each type of criteria that officially count the patient as “in.” Another
would be when you are doing clinical research, where you must be able to report
that participants were all selected according to scientifically studied, reproducible
criteria. And even as an experienced clinician, I return to the actual criteria from
time to time. Perhaps it’s just to have in my mind the complete information that
allows me to communicate with other clinicians; sometimes it is related to my
writing. But mostly, whether I am with patients or talking with students, I stick to
the prototype method—just like nearly every other working clinician.

The Fine Print. Most of the diagnostic material included in these sections is what
I call boilerplate. I suppose that sounds pejorative, but each Fine Print section
actually contains one or more important steps in the diagnostic process. Think of
it this way: The prototype is useful for purposes of inclusion, whereas the
boilerplate is useful largely for the also important exclusion of other disorders and
delimitation from normal. The boilerplate verbiage includes several sorts of
stereotyped phrases and warnings, which as an aid to memory I’ve dubbed the
D’s. (I started out by using “Don’t disregard the D’s” or similar phrases, but soon
got tired of all the typing; so, I eventually adopted “the D’s” as shorthand.)

Differential diagnosis. Here I list all the disorders to consider as alternatives
when evaluating symptoms. In most cases, this list starts off with substance use
disorders and general medical disorders, which despite their relative
infrequency you should always place first on the list of disorders competing for
your consideration. Next I put in those conditions that are most treatable, and
hence should be addressed early. Only at the end do I include those that have
a dismal prognosis, or that you can’t do very much to treat. I call this the safety
principle of differential diagnosis.

13

Distress or disability. Most DSM-5 diagnostic criteria sets require that the
patient experience distress or some form of impairment (in work, social
interactions, interpersonal relations, or something else). The purpose is to
ensure that we discriminate people who are patients from those who, while
normal, perhaps have lives with interesting aspects.

As best I can tell, distress receives one definition in all of DSM-5 (Campbell’s Psychiatric Dictionary
doesn’t even list it). The DSM-5 sections on trichotillomania and excoriation (skin-picking) disorder
both describe distress as including negative feelings such as embarrassment and forfeiture of control.
It’s unclear, however, whether the same definition is employed anywhere else, or what might be the
dominant thinking throughout the manual. But for me, some combination of lost pride, shame, and
control works pretty well as a definition. (DSM-IV didn’t define distress anywhere.)

Duration. Many disorders require that symptoms be present for a certain
minimum length of time before they can be diagnosed. Again, this is to ensure
that we don’t go around indiscriminately handing out diagnoses to everyone.
For example, nearly everyone will feel blue or down at one time or another; to
qualify for a diagnosis of a depressive disorder, it has to hang on for at least a
couple of weeks.

Demographics. A few disorders are limited to certain age groups or genders.

Coding Notes. Many of the Essential Features listings conclude with these notes,
which supply additional information about specifiers, subtypes, severity, and
other subjects relevant to the disorder in question.

Here you’ll find information about specifying subtypes and judging severity for
different disorders. I’ve occasionally put in a signpost pointing to a discussion of
principles you can use to determine that a disorder is caused by the use of
substances.

Sidebars. To underscore or augment what you need to know, I have sprinkled
sidebar information throughout the text (such as the one above). Some of these
merely highlight information that will help you make a diagnosis quickly. Some
contain historical information and other sidelights about diagnoses that I’ve
found interesting. Many include editorial asides—my opinions about patients, the
diagnostic process, and clinical matters in general.

Vignettes. I have based this book on that reliable device, the clinical vignette. As
a student, I found that I often had trouble keeping in mind the features of

14

diagnosis (such as it was back then). But once I had evaluated and treated a
patient, I always had a mental image to help me remember important points
about symptoms and differential diagnosis. I hope that the more than 130
patients I have described in DSM-5 Made Easy will do the same for you.

Evaluation. This section summarizes my thinking for every patient I’ve written
about. I explain how the patient fits the diagnostic criteria and why I think other
diagnoses are unlikely. Sometimes I suggest that additional history or medical or
psychological testing should be obtained before a final diagnosis is given. The
conclusions stated here allow you to match your thinking against mine. There are
two ways you can do this. One is by picking out from the vignette the Essential
Features I’ve listed for each diagnosis. But when you want to follow the thinking
of the folks who wrote the actual DSM-5, I’ve also included references (in
parentheses) to the individual criteria. If you disagree with any of my
interpretations, I hope you’ll e-mail me ([email protected]). And for updated
information, visit my website: www.guilford.com/jm.

Final diagnosis. Usually code numbers are assigned in the record room, and we
don’t have to worry too much about them. That’s fortunate, for they are
sometimes less than perfectly logical. But to tell the record room folks how to
proceed, we need to put all the diagnostic material that seems relevant into
verbiage that conforms to the approved format. My final diagnoses not only
explain how I’d code each patient; they also provide models to use in writing up
the diagnoses for your own patients.

Tables. I’ve included a number of tables to try to give you an overall picture of
various topics—the variety of specifiers that apply across different diagnoses, a list
of physical disorders that can produce emotional and behavioral symptoms.
Those that are of principal use in a given chapter I’ve included in that chapter. A
few, which apply more generally throughout the book, you’ll find in the
Appendix.

My writing. Throughout, I’ve tried to use language that is as simple as possible.
My goal has been to make the material sound as though it was written by a
clinician for use with patients, not by a lawyer for use in court. Wherever I’ve
failed, I hope you will e-mail me to let me know. At some point, I’ll try to put it
right, either in a future edition or on my website (or both).

15

STRUCTURE OF DSM-5 MADE EASY

The first 18 chapters* of this book contain descriptions and criteria for the major
mental diagnoses and personality disorders. Chapter 19 comprises information
concerning other terms that you may find useful. Many of these are Z-codes
(ICD-9 calls them V-codes), which are conditions that are not mental disorders
but may require clinical attention anyway. Most noteworthy are the problems
people with no actual mental disorder have in relating to one another.
(Occasionally, you might even list a Z-code/V-code as the reason a patient was
referred for evaluation.) Also described here are codes that indicate medications’
effects, malingering, and the need for more diagnostic information.

Chapter 20 contains a very brief description of diagnostic principles, followed
by some additional case vignettes, which are generally more complicated than
those presented earlier in the book. I’ve annotated these case histories to help you
to review the diagnostic principles and criteria covered previously. Of course, I
could include only a small fraction of all DSM-5 diagnoses in this section.

Throughout the book, I have tried to give you clinically relevant and accessible
information, written in simple, declarative sentences that describe what you need
to know in diagnosing a patient.

QUIRKS

Here are a few comments regarding some of my idiosyncrasies.

Abbreviations. I’ll cop to using some nonstandard abbreviations, especially for
the names of disorders. For example, BPsD (for brief psychotic disorder) isn’t
something you’ll read elsewhere, certainly not in DSM-5. I’ve used it and others
for the sake of shortening things up just a bit, and thus perhaps reducing ever so
slightly the amount of time it takes to read all this stuff. I use these ad hoc
abbreviations just in the sections about specific disorders, so don’t worry about
having to remember them longer than the time you’re reading about these
disorders. Indeed, I can think of two disorders that are sometimes abbreviated
CD and four that are sometimes abbreviated SAD, so always watch for context.

My quest for shortening has also extended to the chapter titles. In the service
of seeming inclusive, DSM-5 has sometimes overcomplicated these names, in my
view. So you’ll find that I’ve occasionally (not always—I’ve got my obsessive–
compulsive disorder under control!) shortened them up a bit for convenience.

16

You shouldn’t have any problem knowing where to turn for sleep disorders
(which DSM-5 calls sleep–wake disorders), mood disorders (bipolar and related
disorders plus depressive disorders), psychotic (schizophrenia spectrum and other
psychotic) disorders, cognitive (neurocognitive) disorders, substance (substance-
related and addictive) disorders, eating (feeding and eating) disorders, and
various other disorders from which I’ve simply dropped and related from the
official titles. Similarly, I’ve sometimes dropped the /medication from
substance/medication-induced [just about anything].

{Curly braces}. I’ve used these in the Essential Features and in some tables to
indicate when there are two mutually exclusive specifier choices, such as {with}
{without} good prognostic features. Again, it just shortens things up a bit.

Severity specifiers. One of the issues with DSM-5 is its use of complicated
severity specifiers that differ from one chapter to another, and sometimes from
one disorder to the next. Some of these are easier to use than others.

For example, for the psychoses, we are offered the Clinician-Rated Dimensions
of Psychosis Symptom Severity (CRDPSS?), which asks us to rate on a 5-point
scale, based on the past 7 days, each of eight symptoms (the five psychosis
symptoms of schizophrenia [p. 58] plus impaired cognition, depression, and
mania); there is no overall score, only the eight individual components, which we
are encouraged to rate again every few days. My biggest complaint about this
scale, apart from its complexity and the time required, is that it gives us no
indication as to overall functioning—only the degree to which the patient
experiences each of the eight symptoms. Helpfully, DSM-5 informs us that we are
allowed to rate the patient “without using this severity specifier,” an offer that
many clinicians will surely rush to accept.

Evaluating functionality. Whatever happened to the Global Assessment of
Functioning (GAF)? In use from DSM-III-R through DSM-IV-TR, the GAF was
a 100-point scale that reflected the patient’s overall occupational, psychological,
and social functioning—but not physical limitations or environmental problems.
The scale specified symptoms and behavioral guidelines to help us determine our
patients’ GAF scores. Perhaps because of the subjectivity inherent in this scale, its
greatest usefulness lay in tracking changes in a patient’s level of functioning across
time. (Another problem: It was a mash-up of severity, disability, suicidality, and
symptoms.)

However, the GAF is now G-O-N-E, eliminated for several reasons (as
described in a 2013 talk by Dr. William Narrow, research director for the DSM-5
Task Force). Dr. Narrow (accurately) pointed out that the GAF mixed concepts

17

(psychosis with suicidal ideas, for example) and that it had problems with
interrater reliability. Furthermore, what’s really wanted is a disability rating that
helps us understand how well a patient can fulfill occupational and social
responsibilities, as well as generally participate in society. For that, the Task Force
recommends the World Health Organization Disability Assessment Schedule,
Version 2.0 (WHODAS 2.0), which was developed for use with clinical as well as
general populations and has been tested worldwide. DSM-5 gives it on page 747;
it can also be accessed online (www.who.int/classifications/icf/whodasii/en/). It is
scored as follows: 1 = none, 2 = mild, 3 = moderate, 4 = severe, and 5 = extreme.
Note that scoring systems for the two measures are reciprocal; a high GAF score
more or less equates with a low WHODAS 2.0 rating.

After quite a bit of experimentation, I decided that the WHODAS 2.0 is so
heavily weighted toward physical abilities that it poorly reflects the qualities
mental health clinicians are interested in. Some of the most severely ill mental
patients received a only a moderate WHODAS 2.0 score; for example, Velma
Dean scored 20 on the GAF but 1.6 on the WHODAS 2.0. In addition,
calculation of the WHODAS 2.0 score rests on the answers given by the patient
(or clinician) to 36 questions—a burden of data collection that many busy
professionals will not be able to carry. And, because these answers cover
conditions over the previous month, the score cannot accurately represent
patients with rapidly evolving mental disorders. The GAF, on the other hand, is a
fairly simple (if subjective) way to estimate severity.

So, after much thought, I’ve decided not to recommend the WHODAS 2.0
after all. (Anyone who is interested in further discussion can write to me; I’ll be
happy to send along a chart that compares the GAF with the WHODAS 2.0 for
every patient mentioned in this book.) Rather, here’s my fix as regards evaluating
function and severity, and it’s the final quirk I’ll mention: Go ahead and use the
GAF. Nothing says that we can’t, and I find it sometimes useful for tracking a
patient’s progress through treatment. It’s quick, easy (OK, it’s also subjective), and
free. You can specify the patient’s current level of functioning, or the highest level
in any past time frame. You’ll find it in the Appendix of this book.

USING THIS BOOK

There are several ways in which you might use DSM-5 Made Easy.

Studying a diagnosis. Of course, you might go about this in several ways, but

18

here’s how I’d do it. Scan the introductory information for some background,
then read the vignette. Next, compare the information in the vignette to the
Essential Features, to assure yourself that you can pick out what’s important
diagnostically. If you want to see how well the vignettes fit the actual DSM-5
criteria, read through the vignette evaluations; there I’ve touched upon each
of the important diagnostic points. In each evaluation section, you’ll also find a
discussion of the differential diagnosis, as well as some other conditions often
found in association with the disorder in question.

Evaluating a patient whose diagnosis you think you know. Read through the
Essential Features, then check the information you have on this patient against
the prototype. Assign a 1–5 score, using the key given above (p. 3). Check
through the D’s to make sure you’ve considered all disqualifying information
and relevant alternative diagnoses. If all’s well and you’ve hit the mark, I’d
also read through the evaluation section of the relevant vignette, just to make
sure you’ve understood the criteria. Then you might want to read the
introductory material for background.

Evaluating a new patient. Follow the sequence given just above, with one
exception: After identifying one of several areas of clinical interest as a
diagnostic possibility—let’s say an anxiety disorder—you might want to start
with the Quick Guide in the relevant chapter. There you will find capsule
statements (too brief even to be …

DIAGNOSTIC AND STATISTICAL
MANUAL OF

MENTAL DISORDERS
F I F T H E D I T I O N

DSM-5TM

American Psychiatric Association

Officers 2012-2013
P residen t D ilip V. J este, M.D.

P resid en t-Elect J effrey A. Lieberm a n , M.D.
Tr ea su rer Da v id F a ssler, M.D.

Secreta ry R cxser Peele, M.D.

Assembly
Spea k er R. Sc o tt B en so n , M.D.

S peaker-Elect M elin da L. Yo u n g , M.D.

Board o f Trustees
Jeffrey A ka ka, M .D.

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J effrey G eller, M .D., M .P.H .

M ^ c D a v id G ra ff, M.D.
‘ J ^ e&A. G i^ eneVM.D.
Ju d ith F. Ka sh ta n , M.D.
M o lly K. M c Vo y, M .D.
J a m es E. N in in g er, M.D.
Jo h n M. O ldh a m , M .D.

A lan F. Sc h a tzberg , M.D.
A lik s . W id g e, M .D., P h .D.

E r ik R. V an d erlip, M .D .,
M em ber-in-T raining Tr u stee-E lect

DIAGNOSTIC AND STATISTICAL
MANUAL OF

MENTAL DISORDERS7
F I F T H E D I T I O N

DSM-5TM

New School Library

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Lon d on , E n gland

Copyright © 2013 American Psychiatric Association

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The correct citation for this book is American Psychiatric Association: Diagnostic and Statisti­
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Library of Congress Cataloging-in-Publication Data
Diagnostic and statistical manual of mental disorders : DSM-5. — 5th ed.

p. ; cm.
DSM-5
DSM-V
Includes index.
ISBN 978-0-89042-554-1 (hardcover : alk. paper) — ISBN 978-0-89042-555-8 (pbk. : alk. paper)
I. American Psychiatric Association. II. American Psychiatric Association. DSM-5 Task Force,
m. Title: DSM-5. IV. Title: DSM-V.
[DNLM: 1. Diagnostic and statistical manual of mental disorders. 5th ed. 2. Mental Disorders—
classification. 3. Mental Disorders—diagnosis. WM 15]
RC455.2.C4
616.89Ό75—dc23

2013011061
British Library Cataloguing in Publication Data ^ n
A CIP record is available from the British Library. ^

Text Design—Tammy J. Cordova

Manufacturing—Edwards Brothers Malloy ^

cH

Contents

DSM-5 Classification…………………………………………………………xiii
Preface…………………………………………………………………………….. xli

Section I
DSM-5 Basics

Introduction……………………………………………………………………….. 5

Use of the M anual………………………………………………………………19

Cautionary Statement for Forensic Use of DSM-5………………… 25

Section II
Diagnostic Criteria and Codes

Neurodevelopmental Disorders………………………………………….. 31
Schizophrenia Spectrum and Other Psychotic Disorders……….87
Bipolar and Related Disorders………………………………………….. 123
Depressive Disorders………………………………………………………. 155
Anxiety Disorders………………………………………………………………189
Obsessive-Compulsive and Related Disorders………………….. 235
Trauma- and Stressor-Related Disorders…………………………… 265
Dissociative Disorders…………………………………………………….. 291
Somatic Symptom and Related Disorders…………………………. 309
Feeding and Eating Disorders………………………………………….. 329
Elimination Disorders………………………………………………………. 355
Sleep-Wake Disorders………………………………………………………. 361
Sexual Dysfunctions…………………………………………………………423
Gender Dysphoria…………………………………………………………….451

Disruptive, Impulse-Control, and Conduct Disorders…………..461
Substance-Related and Addictive Disorders……………………… 481
Neurocognitive Disorders…………………………………………………. 591
Personality Disorders………………………………………………………. 645
Paraphilic Disorders………………………………………………………… 685
Other Mental Disorders…………………………………………………… 707

Medication-Induced Movement Disorders
and Other Adverse Effects of M edication……………………….. 709

Other Conditions That May Be a Focus of Clinical Attention .. 715

Section III
Emerging Measures and Models

Assessment Measures…………………………………………………….. 733

Cultural Formulation………………………………………………………… 749

Alternative DSM-5 Model for Personality Disorders…………….761

Conditions for Further Study……………………………………………. 783

Appendix
Highlights of Changes From DSM-IV to DSM -5………………….. 809
Glossary of Technical Term s……………………………………………. 817
Glossary of Cultural Concepts of Distress…………………………. 833
Alphabetical Listing of DSM-5 Diagnoses and Codes

(ICD-9-CM and ICD-10-CM)……………………………………………. 839
Numerical Listing of DSM-5 Diagnoses and Codes

(ICD-9-CM)………………………………………………………………….. 863
Numerical Listing of DSM-5 Diagnoses and Codes

(ICD-10-CM)………………………………………………………………….877
DSM-5 Advisors and Other Contributors…………………………… 897

Index………………………………………………………………………………. 917

DSM-5 Task Force
D a vid J. K u pfer, M.D.

Task Force Chair
D a rrel A. R egier, M .D., M .P.H .

Task Force Vice-Chair
William E. Narrow, M.D.,

Research Director

Dan G. Blazer, M.D., Ph.D., M.P.H.
Jack D. Burke Jr., M.D., M.P.H.
William T. Carpenter Jr., M.D.
F. Xavier Castellanos, M.D.
Wilson M. Compton, M.D., M.P.E.
Joel E. Dimsdale, M.D.
Javier I. Escobar, M.D., M.Sc.
Jan A. Fawcett, M.D.
Bridget F. Grant, Ph.D., Ph.D. (2009-)
Steven E. Hyman, M.D. (2007-2012)
Dilip V. Jeste, M.D. (2007-2011)
Helena C. Kraemer, Ph.D.
Daniel T. Mamah, M.D., M.P.E.
James P. McNulty, A.B., Sc.B.
Howard B. Moss, M.D. (2007-2009)

Susan K. Schultz, M.D., Text Editor
Emily A. Kuhl, Ph.D., APA Text Editor

Charles P. O’Brien, M.D., Ph.D.
Roger Peele, M.D.
Katharine A. Phillips, M.D.
Daniel S. Pine, M.D.
Charles F. Reynolds III, M.D.
Maritza Rubio-Stipec, Sc.D.
David Shaffer, M.D.
Andrew E. Skodol II, M.D.
Susan E. Swedo, M.D.
B. Timothy Walsh, M.D.
Philip Wang, M.D., Dr.P.H. (2007-2012)
William M. Womack, M.D.
Kimberly A. Yonkers, M.D.
Kenneth J. Zucker, Ph.D.
Norman Sartorius, M.D., Ph.D., Consultant

APA Division of Research Staff on DSIVI-5
Darrel A. Regier, M.D., M.P.H.,

Director, Division o f Research
William E. Narrow, M.D., M.P.H.,

Associate Director
Emily A. Kuhl, Ph.D., Senior Science

Writer; Staff Text Editor
Diana E. Clarke, Ph.D., M.Sc., Research

Statistician

Lisa H. Greiner, M.S.S.A., DSM-5 Field
Trials Project Manager

Eve K. Moscicki, Sc.D., M.P.H.,
Director, Practice Research Network

S. Janet Kuramoto, Ph.D. M.H.S.,
Senior Scientific Research Associate,
Practice Research Network

Amy Porfiri, M.B.A.
Director o f Finance and Administration

Jennifer J. Shupinka, Assistant Director,
DSM Operations

Seung-Hee Hong, DSM Senior Research
Associate

Anne R. Hiller, DSM Research Associate
Alison S. Beale, DSM Research Associate
Spencer R. Case, DSM Research Associate

Joyce C. West, Ph.D., M.P.P.,
Health Policy Research Director, Practice
Research Network

Farifteh F. Duffy, Ph.D.,
Quality Care Research Director, Practice
Research Network

Lisa M. Countis, Field Operations
Manager, Practice Research Network

Christopher M. Reynolds,
Executive Assistant

APA Office of the IVIedlcal Director
Jam es H. S c u l l y Jr ., M.D.

Medical Director and CEO

Editorial and Coding Consultants
Michael B. First, M.D. Maria N. Ward, M.Ed., RHIT, CCS-P

DSM-5 Work Groups
ADHD and Disruptive Behavior Disorders

D a v id Sha ffer, M.D.
Chair

F. Xa v ier C a stella n o s, M.D.
Co-Chair

Paul J. Frick, Ph.D., Text Coordinator Luis Augusto Rohde, M.D., Sc.D.
Glorisa Canino, Ph.D. Rosemary Tannock, Ph.D.
Terrie E. Moffitt, Ph.D. Eric A. Taylor, M.B.
Joel T. Nigg, Ph.D. Richard Todd, Ph.D., M.D. (d. 2008)

Anxiety, Obsessive-Compulsive Spectrum, Posttraumatic,
and Dissociative Disorders

K a th a rin e A. Ph illips, M.D.
Chair

Michelle G. Craske, Ph.D., Text Scott L. Rauch, M.D.
Coordinator H. Blair Simpson, M.D., Ph.D.

J. Gavin Andrews, M.D. David Spiegel, M.D.
Susan M. Bögels, Ph.D. Dan J. Stein, M.D., Ph.D.
Matthew J. Friedman, M.D., Ph.D. Murray B. Stein, M.D.
Eric Hollander, M.D. (2007-2009) Robert J. Ursano, M.D.
Roberto Lewis-Fernandez, M.D., M.T.S. Hans-Ulrich Wittchen, Ph.D.
Robert S. Pynoos, M.D., M.P.H.

Childhood and Adolescent Disorders
D an iel S. Pin e, M.D.

Chair
Ronald E. Dahl, M.D. James F. Leckman, M.D.
E. Jane Costello, Ph.D. (2007-2009) Ellen Leibenluft, M.D.
Regina Smith James, M.D. Judith H. L. Rapoport, M.D.
Rachel G. Klein, Ph.D. Charles H. Zeanah, M.D.

Eating Disorders
B. T im o th y W alsh, M.D.

Chair
Stephen A. Wonderlich, Ph.D., Richard E. Kreipe, M.D.

Text Coordinator Marsha D. Marcus, Ph.D.
Evelyn Attia, M.D. James E. Mitchell, M.D.
Anne E. Becker, M.D., Ph.D., Sc.M. Ruth H. Striegel-Moore, Ph.D.
Rachel Bryant-Waugh, M.D. G. Terence Wilson, Ph.D.
Hans W. Hoek, M.D., Ph.D. Barbara E. Wolfe, Ph.D. A.P.R.N.

Mood Disorders
J a n a . F a w c e t t , M.D.

Chair
Ellen Frank, Ph.D., Text Coordinator
Jules Angst, M.D. (2007-2008)
William H. Coryell, M.D.
Lori L. Davis, M.D.
Raymond J. DePaulo, M.D.
Sir David Goldberg, M.D.
James S. Jackson, Ph.D.

Kenneth S. Kendler, M.D., Ph.D.
(2007-2010)

Mario Maj, M.D., Ph.D.
Husseini K. Manji, M.D. (2007-2008)
Michael R. Phillips, M.D.
Trisha Suppes, M.D., Ph.D.
Carlos A. Zarate, M.D.

Neurocognitive Disorders
D ilip V. Je s te , M .D. (2007-2011)

Chair Emeritus
D an G. Bla zer, M .D., P h .D., M.P.H.

Chair
R o n a l d C. P e te r s e n , M .D., Ph.D.

Co-Chair
Mary Ganguli, M.D., M.P.H.,

Text Coordinator
Deborah Blacker, M.D., Sc.D.
Warachal Faison, M.D. (2007-2008)

Igor Grant, M.D.
Eric J. Lenze, M.D.
Jane S. Paulsen, Ph.D.
Perminder S. Sachdev, M.D., Ph.D.

Neurodevelopmental Disorders
Su sa n E. Sw ed o , M.D.

Chair
Gillian Baird, M.A., M.B., B.Chir.,

Text Coordinator
Edwin H. Cook Jr., M.D.
Francesca G. Happé, Ph.D.
James C. Harris, M.D.
Walter E. Kaufmann, M.D.
Bryan H. King, M.D.

Catherine E. Lord, Ph.D.
Joseph Piven, M.D.
Sally J. Rogers, Ph.D.
Sarah J. Spence, M.D., Ph.D.
Fred Volkmar, M.D. (2007-2009)
Amy M. Wetherby, Ph.D.
Harry H. Wright, M.D.

Personality and Personality Disorders^
A n d rew E. Sk o d o l, M.D.

Chair
Joh n M. O l d h a m , M.D.

Co-Chair
Robert F. Krueger, Ph.D., Text

Coordinator
Renato D. Alarcon, M.D., M.P.H.
Carl C. Bell, M.D.
Donna S. Bender, Ph.D.

Lee Anna Clark, Ph.D.
W. John Livesley, M.D., Ph.D. (2007-2012)
Leslie C. Morey, Ph.D.
Larry J. Siever, M.D.
Roel Verheul, Ph.D. (2008-2012)

̂The members of the Personality and Personality Disorders Work Group are responsible for the
alternative DSM-5 model for personality disorders that is included in Section III. The Section II
personality disorders criteria and text (with updating of the text) are retained from DSM-IV-TR.

Psychotic Disorders
W illiam T. C arpen ter J r ., M.D.

Chair
Deanna M. Barch, Ph.D., Text Dolores Malaspina, M.D., M.S.P.H.

Coordinator Michael J. Owen, M.D., Ph.D.
Juan R. Bustillo, M.D. Susan K. Schultz, M.D.
Wolfgang Gaebel, M.D. Rajiv Tandon, M.D.
Raquel E. Gur, M.D., Ph.D. Ming T. Tsuang, M.D., Ph.D.
Stephan H. Heckers, M.D. Jim van Os, M.D.

Sexual and Gender Identity Disorders
K en n eth J. Zu c k er, Ph .D.

Chair
Lori Brotto, Ph.D., Text Coordinator Martin P. Kafka, M.D.
Irving M. Binik, Ph.D. Richard B. Krueger, M.D.
Ray M. Blanchard, Ph.D. Niklas Langström, M.D., Ph.D.
Peggy T. Cohen-Kettenis, Ph.D. Heino F.L. Meyer-Bahlburg, Dr. rer. nat.
Jack Drescher, M.D. Friedemann Pfäfflin, M.D.
Cynthia A. Graham, Ph.D. Robert Taylor Segraves, M.D., Ph.D.

Sleep-Wake Disorders
C h a rles F. Reyn o ld s III, M.D.

Chair
Ruth M. O’Hara, Ph.D., Text Coordinator Kathy P. Parker, Ph.D., R.N.
Charles M. Morin, Ph.D. Susan Redline, M.D., M.P.H.
Allan I. Pack, Ph.D. Dieter Riemann, Ph.D.

Somatic Symptom Disorders
J o el E. D im sd a le, M.D.

Chair
James L. Levenson, M.D., Text Michael R. Irwin, M.D.

Coordinator Francis J. Keefe, Ph.D. (2007-2011)
Arthur J. Barsky III, M.D. Sing Lee, M.D.
Francis Creed, M.D. Michael Sharpe, M.D.
Nancy Frasure-Smith, Ph.D. (2007-2011) Lawson R. Wulsin, M.D.

Substance-Related Disorders
C h a rles P. O ‘B rien, M .D., Ph .D.

Chair
Th o m a s J. C ro w ley, M.D.

Co-Chair
Wilson M. Compton, M.D., M.P.E., Thomas R. Kosten, M.D. (2007-2008)

Text Coordinator Walter Ling, M.D.
Marc Auriacombe, M.D. Spero M. Manson, Ph.D. (2007-2008)
Guilherme L. G. Borges, M.D., Dr .Sc. A. Thomas McLellan, Ph.D. (2007-2008)
Kathleen K. Bucholz, Ph.D. Nancy M. Petry, Ph.D.
Alan J. Budney, Ph.D. Marc A. Schuckit, M.D.
Bridget F. Grant, Ph.D., Ph.D. Wim van den Brink, M.D., Ph.D.
Deborah S. Hasin, Ph.D. (2007-2008)

DSM-5 Study Groups
Diagnostic Spectra and DSM/ICD Harmonization

Steven E. H ym a n , M.D.
Chair (2007-2012)

William T. Carpenter Jr., M.D. William E. Narrow, M.D., M.P.H.
Wilson M. Compton, M.D., M.P.E. Charles P. O’Brien, M.D., Ph.D.
Jan A. Fawcett, M.D. John M. Oldham, M.D.
Helena C. Kraemer, Ph.D. Katharine A. Phillips, M.D.
David J. Kupfer, M.D. Darrel A. Regier, M.D., M.P.H.

Lifespan Developmental Approaches
E ric J. L en ze, M.D.

Chair
Susa n K. Sc h u ltz, M.D.

Chair Emeritus
Dan iel S. P in e, M.D.

Chair Emeritus
Dan G. Blazer, M.D., Ph.D., M.P.H.
F. Xavier Castellanos, M.D.
Wilson M. Compton, M.D., M.P.E.

Daniel T. Mamah, M.D., M.P.E.
Andrew E. Skodol II, M.D.
Susan E. Swedo, M.D.

Gender and Cross-Cultural Issues
K im berly A. Yo n kers, M.D.

Chair
R oberto L ew is-Fern â n d ez, M .D., M .T.S.

Co-Chair, Cross-Cultural Issues
Renato D. Alarcon, M.D., M.P.H.
Diana E. Clarke, Ph.D., M.Sc.
Javier I. Escobar, M.D., M.Sc.
Ellen Frank, Ph.D.
James S. Jackson, Ph.D.
Spiro M. Manson, Ph.D. (2007-2008)
James P. McNulty, A.B., Sc.B.

Leslie C. Morey, Ph.D.
William E. Narrow, M.D., M.P.H.
Roger Peele, M.D.
Philip Wang, M.D., Dr.P.H. (2007-2012)
William M. Womack, M.D.
Kermeth J. Zucker, Ph.D.

Psychiatric/General Medical Interface
L a w so n R. W u lsin, M.D.

Chair
Ronald E. Dahl, M.D.
Joel E. Dimsdale, M.D.
Javier I. Escobar, M.D., M.Sc.
Dilip V. Jeste, M.D. (2007-2011)
Walter E. Kaufmann, M.D.

Richard E. Kreipe, M.D.
Ronald C. Petersen, Ph.D., M.D.
Charles F. Reynolds III, M.D.
Robert Taylor Segraves, M.D., Ph.D.
B. Timothy Walsh, M.D.

Impairment and Disability
J a n e S. P a u ls e n , Ph.D .

Chair
J. Gavin Andrews, M.D.
Glorisa Canino, Ph.D.
Lee Anna Clark, Ph.D.
Diana E. Clarke, Ph.D., M.Sc.
Michelle G. Craske, Ph.D.

Hans W. Hoek, M.D., Ph.D.
Helena C. Kraemer, Ph.D.
William E. Narrow, M.D., M.P.H.
David Shaffer, M.D.

Diagnostic Assessment Instruments
J a ck D. Burk e Jr ., M .D., M .P.H.

Chair
Lee Anna Clark, Ph.D.
Diana E. Clarke, Ph.D., M.Sc.
Bridget F. Grant, Ph.D., Ph.D.

Helena C. Kraemer, Ph.D.
William E. Narrow, M.D., M.P.H.
David Shaffer, M.D.

DSM-5 Research Group
W illiam E. N a rro w , M .D., M.P.H.

Chair
Jack D. Burke Jr., M.D., M.P.H.
Diana E. Clarke, Ph.D., M.Sc.
Helena C. Kraemer, Ph.D.

David J. Kupfer, M.D.
Darrel A. Regier, M.D., M.P.H.
David Shaffer, M.D.

Course Specifiers and Glossary
W o lfg a n g G a ebel, M.D.

Chair
Ellen Frank, Ph.D.
Charles P. O’Brien, M.D., Ph.D.
Norman Sartorius, M.D., Ph.D.,

Consultant
Susan K. Schultz, M.D.

Dan J. Stein, M.D., Ph.D.
Eric A. Taylor, M.B.
David J. Kupfer, M.D.
Darrel A. Regier, M.D., M.P.H.

Before each disorder name, ICD-9-CM codes are provided, followed by ICD-IO-CM codes
in parentheses. Blank lines indicate that either the ICD-9-CM or the ICD-IO-CM code is not
applicable. For some disorders, the code can be indicated only according to the subtype or
specifier.

ICD-9-CM codes are to be used for coding purposes in the United States through Sep­
tember 30,2014. ICD-IO-CM codes are to be used starting October 1,2014.

Following chapter titles and disorder names, page numbers for the corresponding text
or criteria are included in parentheses.

Note for all mental disorders due to another medical condition: Indicate the name of
the other medical condition in the name of the mental disorder due to [the medical condi­
tion]. The code and name for the other medical condition should be listed first immedi­
ately before the mental disorder due to the medical condition.

Neurodevelopm ental Disorders (31)

Intellectual Disabilities (33)
319 (___.__) Intellectual Disability (Intellectual Developmental Disorder) (33)

Specify current severity;
(F70) Mild
(F71) Moderate
(F72) Severe
(F73) Profound

315.8 (F88) Global Developmental Delay (41)

319 (F79) Unspecified Intellectual Disability (Intellectual Developmental
Disorder) (41)

Communication Disorders (41)
315.39 (F80.9) Language Disorder (42)

315.39 (F80.0) Speech Sound Disorder (44)

315.35 (F80.81) Childhood-Onset Fluency Disorder (Stuttering) (45)
Note: Later-onset cases are diagnosed as 307.0 (F98.5) adult-onset fluency

disorder.
315.39 (F80.89) Social (Pragmatic) Communication Disorder (47)

307.9 (F80.9) Unspecified Communication Disorder (49)

Autism Spectrum Disorder (50)
299.00 (F84.0) Autism Spectrum Disorder (50)

Specify if: Associated with a known medical or genetic condition or envi­
ronmental factor; Associated with another neurodevelopmental, men­
tal, or behavioral disorder

Specify current severity for Criterion A and Criterion B: Requiring very
substantial support. Requiring substantial support. Requiring support

Specify if: With or without accompanying intellectual impairment. With
or without accompanying language impairment. With catatonia (use
additional code 293.89 [F06.1])

Attention-Deficit/Hyperactivity Disorder (59)
___.__ (__ .__) Attention-Deficit/Hyperactivity Disorder (59)

Specify whether:
314.01 (F90.2) Combined presentation
314.00 (F90.0) Predominantly inattentive presentation
314.01 (F90.1) Predominantly hyperactive/impulsive presentation

Specify if: In partial remission
Specify current severity: Mild, Moderate, Severe

314.01 (F90.8) Other Specified Attention-Deficit/Hyperactivity Disorder (65)

314.01 (F90.9) Unspecified Attention-Deficit/Hyperactivity Disorder (66)

Specific Learning Disorder (66)
___.__ (___.__) Specific Learning Disorder (66)

Specify if:
315.00 (F81.0) With impairment in reading {specify if with word reading

accuracy, reading rate or fluency, reading comprehension)
315.2 (F81.81 ) With impairment in written expression {specify if with spelling

accuracy, grammar and punctuation accuracy, clarity or
organization of written expression)

315.1 (F81.2) With impairment in mathematics {specify if with number sense,
memorization of arithmetic facts, accurate or fluent
calculation, accurate math reasoning)

Specify current severity: Mild, Moderate, Severe

Motor Disorders (74)
315.4 (F82) Developmental Coordination Disorder (74)

307.3 (F98.4) Stereotypic Movement Disorder (77)
Specify if: With self-injurious behavior. Without self-injurious behavior
Specify if: Associated with a known medical or genetic condition, neuro­

developmental disorder, or environmental factor
Specify current severity: Mild, Moderate, Severe

Tic Disorders
307.23 (F95.2) Tourette’s Disorder (81)

307.22 (F95.1) Persistent (Chronic) Motor or Vocal Tic Disorder (81)
Specify if: With motor tics only. With vocal tics only

307.21 (F95.0) Provisional Tic Disorder (81)

307.20 (F95.8), Other Specified Tic Disorder (85)

307.20 (F95.9) Urispecified Tic Disorder (85)

Other Neurodevelopmental Disorders (86)
315.8 (FSB) Other Specified Neurodevelopmental Disorder (86)

315.9 (F89) Unspecified Neurodevelopmental Disorder (86)

Schizophrenia Spectrum
and Other Psychotic Disorders (87)

The following specifiers apply to Schizophrenia Spectrum and Other Psychotic Disorders
where indicated:
^Specify if: The following course specifiers are only to be used after a 1-year duration of the dis­

order: First episode, currently in acute episode; First episode, currently in partial remission;
First episode, currently in full remission; Multiple episodes, currently in acute episode; Mul­
tiple episodes, currently in partial remission; Multiple episodes, currently in full remission;
Continuous; Unspecified

^Specify if: With catatonia (use additional code 293.89 [F06.1])
^Specify current severity of delusions, hallucinations, disorganized speech, abnormal psycho­

motor behavior, negative symptoms, impaired cognition, depression, and mania symptoms

301.22 (F21)

297.1 (F22)

298.8 (F23)

295.40 (F20.81)

295.90 (F20.9)

295.70 (F25.0)
295.70 (F25.1)

293.81 (F06.2)
293.82 (F06.0)

Schizotypal (Personality) Disorder (90)

Delusional Disorder^’ ̂ (90)
Specify whether: Erotomanie type. Grandiose type. Jealous type. Persecu­

tory type. Somatic type. Mixed type. Unspecified type
Specify if: With bizarre content
Brief Psychotic Disorder^’ ̂ (94)
Specify if: With marked stressor(s). Without marked stressor(s). With

postpartum onset
Schizophreniform Disorder^’ ̂ (96)
Specify if: With good prognostic features. Without good prognostic fea­

tures
Schizophrenia^’ ̂ (99)

Schizoaffective Disorder^’ ̂ (105)
Specify whether:

Bipolar type
Depressive type

Substance/Medication-Induced Psychotic Disorder^ (110)
Note: See the criteria set and corresponding recording procedures for

substance-specific codes and ICD-9-CM and ICD-IO-CM coding.
Specify if: With onset during intoxication. With onset during withdrawal

Psychotic Disorder Due to Another Medical Condition^ (115)
Specify whether:

With delusions
With hallucinations

293.89 (F06.1) Catatonia Associated With Another Mental Disorder (Catatonia
Specifier) (119)

293.89 (F06.1) Catatonic Disorder Due to Another Medical Condition (120)

293.89 (F06.1) Unspecified Catatonia (121)
Note: Code first 781.99 (R29.818) other symptoms involving nervous and

musculoskeletal systems.
298.8 (F28) Other Specified Schizophrenia Spectrum and Other Psychotic

Disorder (122)

298.9 (F29) Unspecified Schizophrenia Spectrum and Other Psychotic
Disorder (122)

Bipolar and Related Disorders (123)
The following specifiers apply to Bipolar and Related Disorders where indicated:

Ŝpecify: With anxious distress (specify current severity: mild, moderate, moderate-severe, severe);
With mixed features; With rapid cycling; With melancholic features; With atypical features;
With mood-congruent psychotic features; With mood-incongruent psychotic features; With
catatonia (use additional code 293.89 [F06.1]); With péripartum onset; With seasonal pattem

296.41
296.42
296.43
296.44
296.45
296.46
296.40
296.40
296.45
296.46
296.40

296.51
296.52
296.53
296.54
296.55
296.56
296.50
296.7

(F31.11)
(F31.12)
(F31.13)
(F31.2)
(F31.73)
(F31.74)
(F31.9)
(F31.0)
(F31.73)
(F31.74)
(F31.9)

(F31.31)
(F31.32)
(F31.4)
(F31.5)
(F31.75)
(F31.76)
(F31.9)
(F31.9)

296.89 (F31.81)

Bipolar I Disorder® (123)
Current or most recent episode manic

Mild
Moderate
Severe
With psychotic features
In partial remission
In full remission
Unspecified

Current or most recent episode hypomanie
In partial remission
In kill remission
Unspecified

Current or most recent episode depressed
Mild
Moderate
Severe
With psychotic features
In partial remission
In full remission
Unspecified

Current or most recent episode unspecified

Bipolar II Disorder® (132)
Specify current or most recent episode: Hypomanie, Depressed
Specify course if full criteria for a mood episode are not currently met: In

partial remission. In full remission
Specify severity if full criteria for a mood episode are not currently met:

Mild, Moderate, Severe

301.13 (F34.0)
y

293.83 (__ ._ )

(F06.33)
(F06.33)
(F06.34)

296.89 (F31.89)

296.80 (F31.9)

Cyclothymic Disorder (139)
Specify if: With anxious distress

Substance/Medication-Induced Bipolar and Related Disorder (142)
Note: See the criteria set and corresponding recording procedures for

substance-specific codes and ICD-9-CM and ICD-IO-CM coding.
Specify if: With onset during intoxication. With onset during withdrawal

Bipolar and Related Disorder Due to Another Medical Condition
(145)

Specify if:
With manic features
With manic- or hypomanic-like episode
With mixed features

Other Specified Bipolar and Related Disorder (148)

Unspecified Bipolar and Related Disorder (149)

Depressive Disorders (155)
The following specifiers apply to Depressive Disorders where indicated:
^Specify: With anxious distress (specify current severity: mild, moderate, moderate-severe,

severe); With mixed features; With melancholic features; With atypical features; With mood-
congruent psychotic features; With mood-incongruent psychotic features; With catatonia
(use additional code 293.89 [F06.1]); With péripartum onset; With seasonal pattern

296.99 (F34.8) Disruptive Mood Dysregulation Disorder (156)

. ( _ ■ ) Major Depressive Disorder® (160)

. ( _ . ) Single episode
296.21 (F32.0) Mild
296.22 (F32.1) Moderate
296.23 (F32.2) Severe
296.24 (F32.3) With psychotic features
296.25 (F32.4) In partial remission
296.26 (F32.5) In full remission
296.20 (F32.9) Unspecified

. ( _ · ) Recurrent episode
296.31 (F33.0) Mild
296.32 (F33.1) Moderate
296.33 (F33.2) Severe
296.34 (F33.3) With psychotic features
296.35 (F33.41) In partial remission
296.36 (F33.42) In full remission
296.30 (F33.9) Unspecified
300.4 (F34.1) Persistent Depressive Disorder (Dysthymia)® (168)

Specify if: In partial remission. In full remission
Specify if: Early onset. Late onset
Specify if: With pure dysthymic syndrome; With persistent major depres­

sive episode; With intermittent major depressive episodes, with current

625.4 (N94.3)

(_ _ ■ _ )

293.83 (__ ._ )

(F06.31)
(F06.32)
(F06.34)

311 (F32.8)

311 (F32.9)

episode; With intermittent major depressive episodes, without current
episode

Specify current severity: Mild, Moderate, Severe
Premenstrual Dysphoric Disorder (171)

Substance/Medication-Induced Depressive Disorder (175)
Note: See the criteria set and corresponding recording procedures for

substance-specific codes and ICD-9-CM and ICD-IO-CM coding.
Specify if: With onset during intoxication. With onset during withdrawal

Depressive Disorder Due to Another Medical Condition (180)
Specify if:

With depressive features
With major depressive-like episode
With mixed features

Other Specified Depressive Disorder (183)

Unspecified Depressive Disorder (184)

Anxiety Disorders (189)
309.21 (F93.0)

312.23 (F94.0)

300.29 (__ ._ )

(F40.218)
(F40.228)
( _ · _ )
(F40.230)
(F40.231)
(F40.232)
(F40.233)
(F40.248)
(F40.298)

300.23 (F40.10)

300.01 (F41.0)

300.22 (F40.00)

300.02 (F41.1)

Separation Anxiety Disorder (190)

Selective Mutism (195)

Specific Phobia (197)
Specify if:

Animal
Natural environment
Blood-injection-injury

Fear of blood
Fear of injections and transfusions
Fear of other medical care
Fear of injury

Situational
Other

Social Anxiety Disorder (Social Phobia) (202)
Specify if: Performance only
Panic Disorder (208)

Panic Attack Specifier (214)

Agoraphobia (217)

Generalized Anxiety Disorder (222)

Substance/Medication-Induced Anxiety Disorder (226)
Note: See the criteria set and corresponding recording procedures for

substance-specific codes and ICD-9-CM and ICD-IO-CM coding.
Specify if: With onset during intoxication. With onset during withdrawal.

With onset after medication use

293.84 (F06.4) Anxiety Disorder Due to Another Medical Condition (230)

300.09 (F41.8) Other Specified Anxiety Disorder (233)

300.00 (F41.9) Unspecified Anxiety Disorder (233)

Obsessive-Compulsive and Related Disorders (235)
The following specifier applies to Obsessive-Compulsive and Related Disorders where indicated:
^Specify if: With good or fair insight. With poor insight. With absent insight/delusional beliefs

300.3 (F42)

300.7 (F45.22)

300.3 (F42)

312.39 (F63.2)

698.4 (L98.1)

(_._J

294.8 (F06.8)

300.3 (F42)

300.3 (F42)

Obsessive-Compulsive Disorder^ (237)
Specify if: Tic-related
Body Dysmorphic Disorder^ (242)
Specify if: With muscle dysmorphia
Hoarding Disorder^ (247)
Specify if: With excessive acquisition
Trichotillomania (Hair-Pulling …